BACKGROUND

This disclosure relates to the field of lighting, more particularly to an automated platform, with automation and machine learning features, for design, fulfillment, deployment, and operation of a lighting installation.

Typically, a critical component for the success of an architectural or interior design project is lighting design. Lighting elements may play a number of important functional roles in a space, including rendering (or failing to render) colors of walls, fabrics, furniture and other elements in a space that is illuminated, engendering emotional reactions of individuals in the space, causing biological effects on individuals in the space, and acting as fixtures that harmonize with other fixtures (or fail to do so) to establish an aesthetic design, among others. However, current workflows for lighting design can be significantly flawed. The market for lighting is highly fragmented, with thousands of fixtures and illumination sources being provided by many different suppliers. In many cases, information about lighting products is unavailable, limited, or inaccurate. For example, online searches, to the extent that they provide any technical information about a lighting fixture, typically provide only rudimentary information about characteristics of a light (such as via IES files that provide some basic information about the number of lumens a lighting product provides over a far field area). As a result, most designers of lighting installations need to use physical samples of a lighting product to determine its properties and evaluate its suitability for a given installation. Lighting fixtures tend to be collected in “sample closets” maintained by designers, sales representatives, and distributors, to which designers travel in order to characterize a lighting fixture in person. This process can take days or weeks for a large lighting installation, and even in that case often produces suboptimal results, because comparisons between products are unscientific, and designers often are not even aware of lighting products that may better suit the needs of a given installation. A need exists for fundamentally different lighting design workflows.

Meanwhile, lighting products and the environments in which they are located are increasingly intelligent. Many lighting fixtures are configured to operate as part of the Internet of Things (IoT), so that they can be connected to networks for communication with remote systems (such as in the cloud), can be controlled remotely, can communicate with each other and with other IoT devices in the same spaces (such as beacons and thermostats), and can operate with some degree of autonomy. However, due to the fragmented nature of the lighting market and the lack of expertise on the part of most designers, most lighting installations do little to take advantage of this increased intelligence. A need exists for methods and systems that enable lighting designers and occupants or owners (the term “occupant” or “owner” referring, except where context indicates otherwise, to encompass customers and clients of designers, building owners, tenants, workers and other parties occupying the spaces in which lighting systems are installed) to design, acquire, install, operate and maintain lighting installations that use intelligent features much more effectively to satisfy a wide variety of requirements.

SUMMARY

Systems and methods are described herein that use a variety of novel information technology components to enable coordination of a fundamentally different workflow for design, acquisition, installation, operation and maintenance of a lighting installation that leverages the intelligence of lighting fixtures, including lighting fixtures that use novel control capabilities that are coordinated with other system components as described herein. References to the platform are intended to encompass, except where the context indicates otherwise, the various methods, systems, components, modules, fixtures, data structures, workflows, and other elements that are coordinated, in various embodiments, to enable the workflow.

In embodiments, a platform for the design of a lighting installation that includes an automated search engine for retrieving and storing a plurality of lighting objects in a lighting object library. The platform includes a lighting design environment providing a visual representation of a lighting space containing lighting space objects and lighting objects. The visual representation is based on properties of the lighting space objects and lighting objects obtained from the lighting object library. The platform also includes a plurality of aesthetic filters configured to permit a designer in the design environment to adjust parameters of a plurality of lighting objects handled in the design environment to provide a desired collective lighting effect using the plurality of lighting objects.

In embodiments, methods and systems for generating a data structure that characterizes a near field illumination pattern generated by a light source includes disposing a surface at at least one distance of a plurality of distances in proximity to a light source so that a first side of the surface is illuminated directly by the light source, capturing, with at least a two-dimensional image sensor, a plurality of luminance values from at least one the side of the surface; and generating a data structure characterizing an illumination field generated by the light source including a pattern of luminance values on at least the first side of the surface for the at least one distance among the plurality of distances.

In embodiments, the data structure further characterizes a positioning of the two-dimensional image sensor. In embodiments, the methods include storing an image captured by the image sensor; and repeating the disposing of the surface, the capturing of the plurality of luminance values, and the generating of the data structure for a plurality of incremental distances between the light source and the surface. In embodiments, the repeating causes storing a plurality of incremental-distance differentiated images of the luminance of the light source.

In embodiments, the methods include storing the images for a plurality of light sources in a library that enables a user to search the library to identify a light source having a desired pattern of illumination. In embodiments, the methods include a pattern matching system for matching at least one image in the library to a specified pattern of illumination for a space and for facilitating identification of at least one light source that provides the specified pattern. In embodiments, the method includes a pattern matching system for matching a specified pattern of illumination for a space with portions of images in the library. In embodiments, the portions of images include an off-axis slice through a portion of the plurality of the incremental-distance differentiated images. In embodiments, the method includes a user interface configured to permit a user to at least one of specify and select a pattern of illumination for a space. In embodiments, the pattern of illumination is automatically provided as an input to the pattern matching system. In embodiments, the plurality of luminance values is stored for the side of the surface that is directly illuminated by the light source. In embodiments, the surface is translucent and wherein the plurality of luminance values is stored for a side of the surface that is opposite the first side that is directly illuminated by the light source. In embodiments, the method includes applying indirect measurement software to generate an area-source model of the light source. In embodiments, disposing a surface includes dynamically disposing the surface with a variable distance device that facilitates varying a distance between the light source and the surface.

In embodiments, the method includes generating a 3D volumetric luminance model from the two-dimensional measurements by arranging the plurality of incremental-distance differentiated images into a three-dimensional shape, in embodiments, each of the images represents a slice of the three-dimensional shape. In embodiments, the method includes converting with a processor at least one of the plurality of luminance values to a measure of luminous flux including values for θ and φ. In embodiments, near field illumination characterization includes luminous flux along θ and φ directions for each of a plurality of points on a surface of a light source, and x and y image sensor location data of each of the plurality of luminance values.

In embodiments, the x and y image sensor location data maps to corresponding x and y location data of the light source. In embodiments, the near field illumination characterization is dependent on at least one of: (i) distance between the light source and the surface, (ii) an angle between a line projected from the light source and a position on the surface associated with one of the plurality of luminance values and a normal to the surface, (iii) an optical property of the surface, and (iv) the captured luminance value associated with the position of the surface. In embodiments, methods and systems for characterizing the near field illumination pattern generated by a light source include a positioning slide for holding a screen and moving the screen among a plurality of distances from the light source; at least a two-dimensional image sensor for capturing luminance values from at least one side of the screen when the screen is illuminated by the light source; and a storage system for storing a plurality of data structures, each data structure representing the luminance values captured by the at least two-dimensional image sensor at a given distance of the positioning slide for a given light source.

In embodiments, the plurality of data structures is stored in a searchable library. In embodiments, the method includes a user interface configured to permit a user to search for a light source having a desired pattern of luminance values. In embodiments, methods and systems of near-field illumination pattern matching, include capturing a plurality of two dimensional images of an illumination effect in an environment illuminated by a light source; storing a portion of the plurality of images in a digital data structure that facilitates distinguishing among the stored data values in each of the plurality of images by a two-dimensional location in an image of the plurality of images and an effective distance of the image from the light source; detecting a specified pattern of illumination of the environment in the digital data structure. In embodiments, the pattern includes a plurality of data values identified by a two-dimensional location value and light source distance value.

In embodiments, at least two of the data values in the specified pattern are located at different light source distance values. In embodiments, the light source distance value varies across a portion of the specified pattern of illumination. In embodiments, the plurality of two-dimensional images is non-co-planar. In embodiments, the images in the plurality of two-dimensional images are substantially parallel. In embodiments, the images in the plurality of two-dimensional images are substantially parallel. In embodiments, the illumination effect is an impact of illumination by the light source on at least one object in the environment. In embodiments, the two-dimensional images include data representing an impact of light from the light source on at least one object in the environment. In embodiments, the detecting includes extracting a plurality of data values from the digital data structure based on the two-dimensional location value and the light source distance value for each of the extracted data values. In embodiments, the light source distance value includes an image identifier. In embodiments, the image identifier facilitates identifying an image of the plurality of images.

In embodiments, methods and systems for identifying a desired lighting source, includes a library of lighting objects including at least one of lighting fixture objects and light source objects. In embodiments, the lighting objects are characterized by lighting properties. In embodiments, the lighting properties include at least one output bloom property that characterizes a luminance pattern provided by an output of a lighting object selected from the library; and a pattern matching system that identifies at least one lighting object in the library based on at least one output bloom property.

In embodiments, the output bloom property includes a shape of the output bloom. In embodiments, the shape of the output bloom is at a specified distance from the lighting object. In embodiments, the shape of the output bloom is determined at an intersection of the light bloom with a surface. In embodiments, the surface includes one of a plane, a column, and a slope. In embodiments, the shape of an output bloom includes a portion of a near field illumination pattern generated by a light object selected from the library.

In embodiments, the shape is a substantially continuous shape. In embodiments, the shape is a discontinuous pattern. In embodiments, the output bloom property includes a portion of at least one of a near field illumination pattern and a far field illumination pattern generated by a light object selected from the library. In embodiments, the output bloom property includes at least one of a color and an intensity of an output bloom. In embodiments, the output bloom property includes a reflection from a surface.

In embodiments, the output bloom property includes a transmission through a surface. In embodiments, the surface is a translucent surface. In embodiments, the surface is a shade of a lighting fixture. In embodiments, the method includes a user interface configured to permit a user to view and select a lighting object based on a display of the output bloom.

In embodiments, the method includes a user interface configured to permit a user to at least one of specify and select a desired output bloom property. In embodiments, the pattern matching system automatically matches at least one lighting object in the library to the desired output bloom property. In embodiments, the pattern matching system is an artificial intelligence classification system. In embodiments, the artificial intelligence system is trained to match output bloom patterns based on a training set of patterns matched by one or more human users. In embodiments, the methods and systems of electronic display rendering of lighting distribution in a three-dimensional space, include modeling light source emissions as a set of light ray-traces that represent light traveling between a light source and an element in the three-dimensional space, and reflections therefrom. In embodiments, the modeling of the reflections is based on the set of ray-traces and at least one reflection characteristic of the element in the three-dimensional space; capturing the light source emissions and the reflections as light volume-data; interpolating at least one of light source emissions and reflections for a plurality of points in the three-dimensional space; determining interactions among the ray-traces and reflections in the three-dimensional space; and rendering in the electronic display the volume-data with the interpolated plurality of points and the interactions among the ray-traces in the three-dimensional space.

In embodiments, modeling includes accounting for at least one of light transparency, absorption and reflection of the element in the three-dimensional space. In embodiments, the electronic display is controlled by a virtual reality display controller. In embodiments, the electronic display is an augmented reality display controlled by an augmented reality display controller. In embodiments, rendering includes rendering near-field lighting artifacts. In embodiments, the near-field lighting artifacts are rendered throughout the three-dimensional space. In embodiments, rendering includes accounting for effects relating to physical characteristics of the light source. In embodiments, the light source includes a plurality of distinct light elements, each distinct light element being associated with a corresponding set of ray-traces. In embodiments, rendering includes rendering effects from each of the plurality of distinct light elements. In embodiments, rendering includes rendering distance-based light source intensity. In embodiments, rendering distance-based light source intensity includes rendering light source intensity fall-off over distance from the light source for each ray-trace in the set of ray-traces.

In embodiments, methods and systems of capturing the light source emissions includes disposing a surface at at least one of a plurality of distances in proximity to the light source so that a first side of the surface is illuminated directly by the light source, capturing, with at least a two-dimensional image sensor, a plurality of luminance values from at least one side of the illuminated surface; generating a data structure characterizing the illumination field generated by the light source including the pattern of luminance values on at least one side of the illuminated surface for the distance among the plurality of distances; storing an image captured by the image sensor; and repeating the disposing, capturing, and generating a data structure steps for a plurality of incremental distances between the light source and the intermediate surface. In embodiments, the repeating causes storing a plurality of incremental-distance differentiated images of the luminance of the light source.

In embodiments, methods and systems of electronic display rendering of lighting distribution in a three-dimensional space, includes modeling light source emissions as a set of light ray-traces that represent light traveling between a light source and an element in the three-dimensional space, and reflections therefrom. In embodiments, the modeling of the reflections is based on the set of ray-traces and at least one reflection characteristic of the element in the three-dimensional space; capturing a plurality of two-dimensional images of at least one of the light source emissions and the reflections; storing a portion of the plurality of images in a digital data structure as light volume-data, the structure facilitates distinguishing among the light volume data in each of the plurality of images by a two-dimensional location in an image of the plurality of images and an effective distance of the image from the light source; interpolating at least one of light source emissions and reflections for a plurality of points in the three-dimensional space; determining interactions among the ray-traces and reflections in the three-dimensional space; and rendering in the electronic display the volume-data with the interpolated plurality of points and the interactions among the ray-traces in the three-dimensional space.

In embodiments, the modeling includes accounting for at least one of light transparency, absorption and reflection of the element in the three-dimensional space. In embodiments, the electronic display is controlled by a virtual reality display controller. In embodiments, the electronic display is an augmented reality display controlled by an augmented reality display controller. In embodiments, the rendering includes rendering near-field lighting artifacts. In embodiments, the near-field lighting artifacts are rendered throughout the three-dimensional space. In embodiments, the rendering includes accounting for effects relating to physical characteristics of the light source. In embodiments, the light source includes a plurality of distinct light elements, each distinct light element being associated with a corresponding set of ray-traces. In embodiments, the rendering includes rendering effects from each of the plurality of distinct light elements. In embodiments, the rendering includes rendering distance-based light source intensity. In embodiments, the rendering distance-based light source intensity includes rendering light source intensity fall-off over distance from the light source for each ray-trace in the set of ray-traces.

In embodiments, methods and systems for enabling custom tuning a lighting object, includes defining a custom tuning profile for the lighting object, the custom tuning profile specifying at least one of a color tuning profile, a dimming profile, and a lighting distribution profile for the lighting object; and automatically, under control of a processor, translating the defined custom tuning profile into a set of instructions for controlling the lighting object to behave according to the custom tuning profile in response to user input.

In embodiments, the custom tuning profile is a dimming profile that specifies a set of points on a color temperature gamut that defines a dimming curve along which the lighting object will dim in response to a control input from a dimmer. In embodiments, the dimming profile is specified to match a known dimming profile of a type of lighting object. In embodiments, the custom tuning profile is a color tuning profile that specifies a set of points on a color gamut through which a lighting object will progress in response to a variable voltage control input. In embodiments, the method includes a user interface configured to permit a user to define the custom tuning profile. In embodiments, the user interface allows a user to specify a dimming profile by tracing a curve on a gamut. In embodiments, the user interface allows a user to specify a color for a color tuning profile from a color gamut.

In embodiments, the method includes a library of stored profiles selectable by a user for tuning of a lighting object. In embodiments, the library of stored profiles includes at least one of a color quality profile, a circadian profile, a concentration profile, a relaxation profile, and an efficacy profile. In embodiments, the library is organized to provide custom tuning profiles that satisfy at least one of a performance requirement and an aesthetic requirement desired by a user. In embodiments, methods and systems of controlling a color of a light source independent of controlling dimming of the light source, include capturing at least one custom color curve for controlling a light source; controlling dimming of the light source through a first input that accepts a voltage that varies between 0 and 10 volts; controlling color of the light source through a second input that accepts a voltage that varies between 0 and 10 volts independent of the first input; and mapping the at least one custom color curve to the second input range of 0 to 10 volts.

In embodiments, the custom color curve is a dimming profile that specifies a set of points on a color temperature gamut that defines a dimming curve along which the light source will dim in response to a control input from a dimmer. In embodiments, the dimming profile is specified to match a known dimming profile of a type of lighting object. In embodiments, the custom color curve is a color tuning profile that specifies a set of points on a color gamut through which the light source will progress in response to a variable voltage control input.

In embodiments, the method includes a library of stored profiles selectable by a user for tuning of a lighting object. In embodiments, the library of stored profiles includes at least one of a color quality profile, a circadian profile, a concentration profile, a relaxation profile, and an efficacy profile. In embodiments, the library is organized to provide custom tuning profiles that satisfy at least one of a performance requirement and an aesthetic requirement desired by a user.

In embodiments, methods and systems for a light source control system for a light source that has independent color and dimming control inputs, and include a first output port of the system that is operatively coupled to the color control input of the light source; a second output port of the system that is operatively coupled to the brightness control input of the light source; and a processor of the system that accesses a light source tuning profile that characterizes a multi-dimensional lighting curve by mapping color output of the light to brightness of the light source so that a change in the brightness input causes a corresponding change in the color input.

In embodiments, the processor controls the first output and the second output based on information in the tuning profile. In embodiments, the controlling the brightness input results in the processor also controlling the color input to adjust the color of the light based on the tuning profile. In embodiments, the controlling the brightness to reduce the brightness results in a warmer color being output by the light source. In embodiments, the controlling the brightness to increase the brightness results in a cooler color being output by the light source. In embodiments, the tuning profile maps a plurality of target color and brightness output values to a corresponding plurality of two-dimensional voltage values, a first dimension controlling light color and a second dimension controlling brightness. In embodiments, the tuning profile maps values in the first dimension to a color control input voltage range. In embodiments, the tuning profile maps values in the second dimension to a brightness control input voltage range.

In embodiments, the tuning profile maps target output color temperatures of the light source to values in the first and second dimensions so that controlling the color input and brightness input based on the first and second dimensions configures the light source to output a target color temperature based on the tuning profile color temperature mapping. In embodiments, the tuning profile facilities maintaining a light color as the light is dimmed by adjusting the light color control based on a change in the light dimming control. In embodiments, the tuning profile is indexed by at least one of biologic impacts and physiological impacts of light so that at least one of the light color and the light brightness is specified for a plurality of biologic impacts and physiological impacts.

In embodiments, methods and systems of using emotional filters for lighting design, include capturing stylistic and aesthetic features from a visual representation of an environment; populating, with the captured features, an installation-specific emotional content data structure; applying machine learning to user feedback about at least one of emotional and aesthetic aspects of installation. In embodiments, the installation is characterized by the installation-specific emotional content data structure. In embodiments, the machine learning facilitates generating an understanding of factors that contribute to each emotional effect of a plurality of emotional effects; and updating at least a portion of the emotional content data structure based on the feedback.

In embodiments, the visual representation includes at least one of a photograph and a video. In embodiments, the method includes selecting at least one light source for the environment based on a similarity of a portion of an emotional content data structure for the light source with a corresponding portion of the installation-specific emotional content data structure. In embodiments, capturing features includes analyzing at least one of images, 3D models, renderings, and scans of the environment. In embodiments, populating includes storing at least one of cultural and geographical data associated with the environment in the installation-specific emotional content data structure. In embodiments, the emotional content data structure includes at least one of objects, classes, and properties including lighting properties selected from a group consisting of distribution of light on lighting space objects, distribution of lights on surfaces, illumination values, color and color temperature of light sources, spectral content, and fixture type. In embodiments, lighting space objects include at least one of desks, tables, and workspaces. In embodiments, the spectral content includes quality and intensity of light at certain spectral ranges. In embodiments, the fixture type includes at least one of modern, retro, industrial, romantic, suspended, embedded, and form factor.

In embodiments, methods and systems of a lighting design system using emotional filters, include a visual representation of an environment; a feature capture facility adapted to capture stylistic and aesthetic features of the environment from the visual representation and populate the captured features into an installation-specific emotional content data structure that is accessible to a processor; machine learning algorithms executing on a processor that receive user feedback about at least one of emotional and aesthetic aspects of an installation characterized by the installation-specific emotional content data structure, the machine learning algorithms generating an understanding of factors that contribute to each emotional effect of a plurality of emotional effects, the processor updating at least a portion of the emotional content data structure based on the feedback.

In embodiments, the visual representation includes at least one of a photograph and a video. In embodiments, the method includes the processor selecting at least one light source for the environment based on a similarity of a portion of an emotional content data structure for the light source with a corresponding portion of the installation-specific emotional content data structure. In embodiments, the feature capture facility is configured to capture stylistic and aesthetic features by analyzing at least one of images, 3D models, renderings, and scans of the environment. In embodiments, to populate the captured features includes storing at least one of cultural and geographical data associated with the environment in the installation-specific emotional content data structure.

In embodiments, the emotional content data structure includes at least one of objects, classes, and properties including lighting properties selected from a group consisting of distribution of light on lighting space objects, distribution of lights on surfaces, illumination values, color and color temperature of light sources, spectral content, and fixture type. In embodiments, lighting space objects include at least one of desks, tables, and workspaces. In embodiments, spectral content includes quality and intensity of light at certain spectral ranges. In embodiments, the fixture type includes at least one of modern, retro, industrial, romantic, suspended, embedded, and form factor. In embodiments, the method includes a library of light source emotional content data structures that describe stylistic and aesthetic features of a plurality of light sources.

In embodiments, the method includes a light source selection facility that compares at least one portion of emotional content data structures in the library with a corresponding at least one portion of an installation-specific emotional content data structure thereby generating a set of candidate light sources for satisfying at least one of aesthetic and stylistic aspects of the environment. In embodiments, information descriptive of at least one of the aesthetic and stylistic aspects of the set of candidate light sources is displayed on an electronic display to enable comparison with each other and with the at least one of aesthetic and stylistic aspects of the environment.

In embodiments, methods and systems of a near-field characterization system include a light source positioning support adapted to hold a light source disposed to distribute light in a first orientation; an intermediate screen disposed to receive on a first side the distributed light, the intermediate screen constructed to transfer a portion of the received light to a second side that is substantially parallel to the first side; a two-dimensional array illumination sensor disposed to capture an image of the second side of the intermediate screen, the image including a data value representing illumination at each of a plurality of image sensing positions in the array; a processor adapted to control the illumination sensor and store the data value and the two-dimensional location of the corresponding image sensing position in the array; and a data storage facility that works with the processor to store the data value and its corresponding position for a plurality of image sensing positions in the array.

In embodiments, to control the illumination sensor includes rotating the illumination sensor. In embodiments, the two-dimensional array illumination sensor includes a digital camera.

In embodiments, the digital camera is a camera function of a smartphone. In embodiments, the intermediate screen is translucent.

In embodiments, the method includes a positioning system of the intermediate screen controlled by the processor to adjust a distance between the light source and the intermediate screen. In embodiments, the method includes a positioning system of the light source controlled by the processor to adjust a distance between the light source and the intermediate screen. In embodiments, the light source positioning support facilitates rotational and translational motion of the light source. In embodiments, the processor is further adapted to control at least one of position, rotation, and translational motion of the light source. In embodiments, the method includes a housing that mitigates the impact of ambient light on the intermediate screen and the area array illumination sensor.

In embodiments, methods and systems for a near-field characterization system include a processor controlled light source positioning support adapted to hold a light source disposed to distribute light in a plurality of orientations, the processor controlling at least a rotation of the light source about a longitudinal axis of the support; an intermediate screen including a first side and a substantially parallel second side, the intermediate screen disposed to receive the distributed light on the first side and constructed to transfer a portion of the received light to the second side; an area array illumination sensor disposed to capture light emissions from the second side of the intermediate screen; a controller adapted to control the illumination sensor and store the data value and the array location of the corresponding image sensing position in a data storage facility.

In embodiments, the method includes a housing that mitigates the impact of ambient light on the intermediate screen and the area array illumination sensor. In embodiments, the housing extends from the second side of the intermediate screen to the area array. In embodiments, the method includes a housing that encloses the light source, the intermediate screen, and the area array. In embodiments, the housing is configured to conditionally eliminate ambient light from reaching the enclosed system elements. In embodiments, the method includes a spectrometer disposed relative to the intermediate screen to capture spectral content of light proximal to the intermediate screen.

In embodiments, the spectrometer is disposed to capture spectral content of light between the light source and the intermediate screen. In embodiments, the spectrometer is disposed to capture spectral content of light between the intermediate screen and the area array sensor. In embodiments, a position and orientation of at least one of the light source, the intermediate screen, and the area array is adjustable under processor control. In embodiments, at least one of the position and orientation of at least one of the light source, intermediate screen, and the area array is adjusted between successive area array light distribution captures. In embodiments, an increment of the adjustment between successive light distribution captures is non-linear. In embodiments, an increment after a light distribution capture is based on an at least one of an entropy and an amount of information captured.

In embodiments, methods and systems for characterizing a near field illumination effect of a light source, the method includes iteratively capturing, with a multi-dimensional image sensor set, an illumination value for each of a plurality of image sensing elements in the image sensor set, for a plurality of distance-specific positions of the light source; storing, in a processor accessible electronic memory, a plurality of images captured by the image sensor set; producing a multi-dimensional representation of the near-field light distribution of light source by processing, with a multi-dimensional near-field illumination reconstruction algorithm, the plurality of stored images and their corresponding distance-specific position values; and storing the multi-dimensional representation in the processor accessible electronic memory.

In embodiments, the image sensor set is a two-dimensional array. In embodiments, the multi-dimensional representation includes four dimensions consisting of a first dimension of the two-dimensional array, a second dimension of the two-dimensional array, a theta component of the corresponding distance-specific position value and a phi component of the corresponding distance-specific position value.

In embodiments, the multi-dimensional representation includes five dimensions consisting of a first dimension of the two-dimensional array, a second dimension of the two-dimensional array, a value representing the distance-specific position of the light source, a theta component of the corresponding distance-specific position value and a phi component of the corresponding distance-specific position value. In embodiments, the reconstruction algorithm determines a relative contribution of each point source on a light source's surface to each pixel in the two-dimensional array image sensor. In embodiments, the producing a multi-dimensional representation includes applying at least one of the Kaczmarz method, numerical methods, machine learning methods, neural networks, and linear algebra. In embodiments, the multi-dimensional array image sensor set includes a smartphone camera. In embodiments, the multi-dimensional representation constitutes a high-fidelity model of the light source. In embodiments, the method includes controlling with the processor, a distance between the light source and the multi-dimensional array image sensor set. In embodiments, iteratively capturing includes capturing a light pattern visible on a secondary side of a translucent intermediate screen disposed between the light source and the array image sensor.

In embodiments, methods and systems for incrementally reconstructing a near-field illumination effect of a light source, the method includes capturing a first occurrence of multi-dimensional luminance of a light source with an indirect luminance collection device disposed at a first position relative to the light source; capturing a second occurrence of multi-dimensional luminance of the light source with the indirect luminance collection device disposed at a second position relative to the light source; producing a representation of the near-field illumination of the light source by applying a multi-dimensional near-field illumination reconstruction algorithm to the captured occurrences of multi-dimensional luminance of the light; storing the representation in a computer accessible non-volatile memory; and repeating the capturing, producing, and storing steps for a plurality of positions relative to the light source, thereby producing a model of near-field illumination of the light source.

In embodiments, the model of near-field illumination includes a plurality of data values for theta and phi luminance values for a plurality of three-dimensional locations in the near-field of the light source. In embodiments, a position relative to the light source includes a distance from the light source, a longitude relative to the light source and a latitude relative to the light source. In embodiments, the plurality of positions includes a plurality of distances for a given longitude and latitude. In embodiments, the plurality of positions includes a plurality of longitudes for a given distance. In embodiments, the plurality of positions includes a plurality of latitudes for a given distance. In embodiments, the reconstruction algorithm determines a contribution of a point source on a surface of the light source for each captured occurrence.

In embodiments, producing a representation of the near-field illumination includes applying at least one of the Kaczmarz method, numerical methods, machine learning methods, neural networks, and linear algebra to the captured occurrences of multi-dimensional luminance of the light. In embodiments, producing a representation of the near-field illumination includes applying at least two of the Kaczmarz method, numerical methods, machine learning methods, neural networks, and linear algebra to the captured occurrences of multi-dimensional luminance of the light. In embodiments, the indirect luminance collection device includes a smartphone camera adapted to capture indirect luminance from the light source. In embodiments, the smartphone camera adapted with a screen attached to the smartphone over the smartphone camera so that light from the light source impacts the smartphone camera indirectly.

In embodiments, a portion of light from the light source passes through the screen. In embodiments, the multi-dimensional representation constitutes a high-fidelity model of the light source. In embodiments, the method includes controlling with the computer, a distance between the light source and the indirect luminance collection device. In embodiments, capturing the occurrences of multi-dimensional luminance of a light source includes capturing a light pattern visible on a secondary side of a translucent intermediate screen disposed between the light source and the indirect luminance collection device.

In embodiments, the near-field illumination reconstruction algorithm produces a five-dimensional representation of the near-field. In embodiments, each value in the near-field is characterized by (i) a distance from a reference position on the indirect luminance collection device to the light source, (ii) a longitudinal offset from the reference point for the occurrence, (iii) a latitudinal offset from the reference point, (iv) a theta value of the illumination, and (v) a phi value for the illumination.

In embodiments, methods and systems include receiving a data structure representative of a desired lighting effect created by the incidence of illumination from a light source on at least one surface; determining characteristics and values thereof of a light source for producing the desired lighting effect; matching the light source characteristics to a library of light sources. In embodiments, at least a portion of the light sources in the library includes at least a portion of the light source characteristics; determining, from a result of the matching, a candidate set of light sources in the library; selecting a portion of the candidate set of light sources based on similarity of the values of the determined characteristics with values of corresponding characteristics in the candidate set of light sources; and presenting the selected light sources in an electronic user interface.

In embodiments, the data in the data structure includes a plurality of luminance values for a lighting effect region. In embodiments, the data in the data structure includes at least one of desired effect of the lighting effect, an aesthetic filter effect of the lighting effect and an emotional filter effect of the lighting effect. In embodiments, the characteristics include light color and light intensity. In embodiments, the electronic user interface facilitates visual comparison of the desired lighting effect and a lighting effect of at least one of the selected light sources. In embodiments, the electronic user interface facilitates presenting the desired lighting effect and a lighting effect of at least one of the selected light sources in an environment. In embodiments, the environment is a live view of an environment and the user interface utilizes augmented reality to present at least one of the desired lighting effect and a lighting effect of at least one of the selected light sources. In embodiments, the luminance values in the plurality of luminance values are dispersed throughout the lighting effect region. In embodiments, the lighting effect region is substantially planar. In embodiments, the user interface further enables a user to search through the library based on the desired lighting effect.

In embodiments, the determining characteristics and values thereof of a light source for producing the desired lighting effect is based on a result of machine learning applied to an algorithm that associates light source characteristics with lighting effects. In embodiments, selecting a portion of the candidate set of light sources based on similarity of the values of the determined characteristics employs weighting of the characteristics. In embodiments, the weighting is determined based on a degree of compliance by the light sources with the desired light lighting effect. In embodiments, a user specifies the degree of compliance through the user interface.

In embodiments, methods and systems include receiving a data structure representative of a desired lighting effect; determining characteristics and values thereof of the desired lighting effect; matching the characteristics to corresponding lighting effect characteristics in a library of lighting effects. In embodiments, each of the lighting effects in the library corresponds to a light source; determining from an output of the matching a candidate set of light sources in the library; selecting a portion of the candidate set of light sources based on similarity of the values of the determined characteristics with values of corresponding characteristics of lighting effects for light sources in the candidate set of light sources; and presenting the selected light sources in an electronic user interface.

In embodiments, the data in the data structure includes a plurality of luminance values for a lighting effect region. In embodiments, the data in the data structure includes at least one of desired effect of the lighting effect, an aesthetic filter effect of the lighting effect and an emotional filter effect of the lighting effect. In embodiments, the characteristics include light color and light intensity. In embodiments, the electronic user interface facilitates visual comparison of the desired lighting effect and a lighting effect of at least one of the selected light sources. In embodiments, the electronic user interface facilitates presenting the desired lighting effect and a lighting effect of at least one of the selected light sources in an environment.

In embodiments, the environment is a live view of an environment and the user interface utilizes augmented reality to present at least one of the desired lighting effect and a lighting effect of at least one of the selected light sources. In embodiments, luminance values in the plurality of luminance values are dispersed throughout the lighting effect region. In embodiments, the lighting effect region is substantially planar. In embodiments, the user interface further enables a user to search through the library based on the desired lighting effect. In embodiments, the determining characteristics and values thereof for producing the desired lighting effect is based on a result of machine learning applied to an algorithm that associates characteristics with lighting effects.

In embodiments, selecting a portion of the candidate set of light sources based on similarity of the values of the determined characteristics employs weighting of the characteristics. In embodiments, weighting is determined based on a degree of compliance by the light sources with the desired lighting effect. In embodiments, a user specifies the degree of compliance through the user interface. In embodiments, methods and systems include collecting data from a plurality of lighting installations; and classifying the lighting installations based on at least one lighting effect created by the installations; and storing at least one property for at least one of a lighting object based on the classification of the effect.

In embodiments, the method includes a library of lighting objects for which effects are classified. In embodiments, the method includes enabling a user to search for a lighting object based on a desired effect sought by the user. In embodiments, classifying is based on a measured effect on at least one of an individual and a group. In embodiments, the measured effect is a productivity effect. In embodiments, the measured effect is a health effect.

In embodiments, classifying includes classifying images of the lighting installations to establish at least one of an aesthetic filter and an emotional filter that characterizes a subset of the lighting installations. In embodiments, classifying occurs by an expert system. In embodiments, classifying occurs by an artificial intelligence system. In embodiments, the artificial intelligence system is trained based on a training set created by having human individuals classifying the lighting installations.

In embodiments, the filter includes a data structure indicating weights for lighting object properties that contribute to the filter. In embodiments, the method includes characterizing at least one lighting object property based on its contribution to the filter. In embodiments, the method includes characterizing at least one lighting object based on its contribution to the filter. In embodiments, methods and systems of lighting, include receiving as a user selection of a filter, an intent of the user; converting the user intent to a set of lighting control parameters; using a lighting control platform to adjust settings on a plurality of light sources in a target environment to reflect the set of lighting control parameters; applying user feedback associated with distributions of lighting in the target environment to a machine learning processor to facilitate developing an understanding of a relationship between user reactions of the lighting environment and the user's intent; and updating, based on the understanding, a data set that facilitates the converting of user intent to lighting controls.

In embodiments, a user intent is to promote user feedback indicative of one of alertness, happiness, and romance. In embodiments, methods and systems of near field metrics for evaluating light sources, includes taking a near field illumination characterization of a light source; processing the characterization with at least one of a pattern detecting algorithm and an artifact detecting algorithm; counting occurrences of detected patterns and artifacts; determining at least one of size and scale of detected artifacts; and aggregating at least one of size of artifacts, scale of artifacts, and occurrences of artifacts, thereby producing at least one near field metric of a plurality of distinct near field metrics for the light source.

In embodiments, the near field metrics are selected from a group consisting of a mixing distance metric, a scale of artifacts metric, and a contrast in near field metric. In embodiments, the scale of artifacts metric includes an indication of at least one of a size, a scale, and a frequency of occurrence of artifacts in a light pattern produced by the light source. In embodiments, mixing distance metric includes an indication of a distance from a light source at which a magnitude of artifacts drops below a threshold of artifact visibility. In embodiments, contrast in near field metric includes an indication of an intensity of at least one of patterns and artifacts detectable proximal to the light source. In embodiments, the indication of intensity includes a minimum to maximum ratio of at least one of detectable patterns and artifacts.

In embodiments, methods and systems for providing near field metrics for characterizing light sources, include accessing a data structure that captures a near field illumination characterization of a light source; calculating metrics for the near field including at least one of light quality rating, light output, color range, color temperature, lighting mixing characteristics and spectral characteristics by processing at least two of three-dimensional position, theta, and phi values for a plurality of data values in the data structure with near-field metrics algorithms; and storing the calculated metrics in a library of light sources so that accessing the light source in the library facilitates accessing the associated near field metrics.

In embodiments, near field metrics are selected from a group consisting of a mixing distance metric, a scale of artifacts metric, and a contrast in near field metric. In embodiments, the scale of artifacts metric includes an indication of at least one of a size, a scale, and a frequency of occurrence of artifacts in a light pattern produced by the light source. In embodiments, mixing distance metric includes an indication of a distance from a light source at which magnitude of artifacts drops below a threshold of artifact visibility. In embodiments, contrast in near field metric includes an indication of an intensity of at least one of patterns and artifacts detectable proximal to the light source. In embodiments, the indication of intensity includes a minimum to maximum ratio of at least one of detectable patterns and artifacts. In embodiments, calculating metrics is based on machine learning algorithms applied to algorithms that associate candidate metrics with a plurality of near field data sets.

In embodiments, methods and systems for augmented reality lighting methods, include a first device representing a light source, a position and orientation of the first device in an environment being detectable by a second device in the environment; the second device capturing an image of at least a portion of the environment based on the detected position and orientation of the first device and communicating the detected position and orientation of the first device and the captured image over a network to a lighting modeling server; the lighting modeling server accessing a lighting model of the light source and modeling an interaction of the light source with elements of the environment detected in the captured image based on the position and orientation of the first device; and the second device receiving the modeled interaction from the lighting modeling server and rendering the modeled interaction in an augmented reality representation of the environment.

In embodiments, the second device detects the orientation and position of the first device by capturing at least one image of the first device in the environment, analyzing the at least one image for indications of the position of the device and its orientation in the environment, and tracking changes to the position and orientation of the device. In embodiments, the second device detects the orientation and position of the first device by analyzing received position and orientation information from the first device. In embodiments, the first device includes a user interface through which a user is enabled to select a light source from a library of light sources and through which an image of the selected light source is presented. In embodiments, the first device is configured to communicate an identifier of the selected light source to the lighting model server. In embodiments, the lighting model server accesses the lighting model for the selected light source from the library.

In embodiments, methods and systems include a first computing device disposed in an environment and rendering in its user interface a selected light fixture, the first device communicating its location and orientation in the environment over a wireless network; and a second device rendering in its user interface an illumination effect of the selected light fixture on a portion of the environment in response to a model of luminance of the selected light fixture, at least one of surfaces and objects in the portion of the environment, and the location and orientation of the first device.

In embodiments, the second device captures at least one image of the portion of the environment based on the location and orientation of the first device. In embodiments, the second device is disposed in the environment. In embodiments, changes to at least one of the position and orientation of the first device cause corresponding changes to the rendering of the illumination effect in the second device. In embodiments, the model of luminance incorporates at least one of near-field and far-field luminance characterization of the selected light fixture.

In embodiments, the second device includes an augmented reality device that renders an illumination effect of the selected light fixture based at least in part on a position and orientation of the second device in the environment. In embodiments, the user interface of the first device is configured to facilitate selecting a light fixture from a light fixture library. In embodiments, the method includes a lighting space modeling server that generates a data set that describes the illumination effect of the selected light fixture on the portion of the environment that the second device uses for rendering. In embodiments, the first device is configured to communicate an identifier of the selected light fixture to the lighting space model server. In embodiments, the lighting space model server accesses the model of luminance of the selected light fixture from the library.

In embodiments, methods and systems of augmented reality-based lighting design, include detecting light sources in an augmented reality image; detecting at least one of surfaces and objects in the augmented reality image; facilitating disposition of at least one virtual light source in the augmented reality image, resulting in an updated augmented reality image; processing a near field and far field luminance characterization of the at least one virtual light source and the updated augmented reality image with a lighting space model; and depicting illumination of portions of the augmented reality image in response to the lighting space model.

In embodiments, portions of the augmented reality image include at least one of the detected surfaces and objects. In embodiments, the method includes detecting lighting effects of the detected light sources. In embodiments, illuminating portions of the augmented reality image is in response to the detected lighting effects.

In embodiments, methods and systems of illumination in an environment, include controlling a first light source in the environment, disposed to illuminate a first region of the environment, to mimic sky color based on at least one of a user input and a time of day; and controlling a second light source to mimic a window on a vertical wall of the environment. In embodiments, the second light source generates a color on the vertical wall that is consistent with the mimicked sky color.

In embodiments, the illuminated first region of the environment includes an area of a ceiling of the environment. In embodiments, the illuminated first region of the environment mimics a skylight. In embodiments, the illuminated first region of the environment includes a plurality of distinct areas of a ceiling of the environment. In embodiments, the illuminated first region of the environment mimics a plurality of distinct skylights. In embodiments, the illuminated first region changes colors based on an estimated position of the sun from sunrise to sunset. In embodiments, the illuminated first region mimics a lunar illumination effect based on a position of the moon. In embodiments, the method includes controlling a third light source to produce a melanopic flux ratio of at least 10:1 in a portion of the environment.

In embodiments, the first light source is disposed in the environment to produce cove lighting. In embodiments, the at least one of the first light source and the second light source is disposed in the environment to produce graze lighting. In embodiments, controlling the first light source and the second light source results in a melanopic flux ratio of at least 10:1 in a portion of the environment.

In embodiments, methods and systems of illumination in an environment, include controlling a first light source in the environment, the first light disposed to illuminate a first region of the environment, the controlled light mimicking sky color based on at least one of a user input and a time of day; and controlling a second light source to illuminate a second region of the environment, the second light source being selected from a library of light sources. In embodiments, the second region of the environment is a workspace.

In embodiments, the illuminated first region of the environment includes an area of a ceiling of the environment. In embodiments, the illuminated first region of the environment mimics a skylight. In embodiments, the illuminated first region of the environment includes a plurality of distinct areas of a ceiling of the environment. In embodiments, the illuminated first region of the environment mimics a plurality of distinct skylights. In embodiments, the illuminated first region changes colors based on an estimated position of the sun from sunrise to sunset and based on an estimated position of the moon from sunset to sunrise. In embodiments, the illuminated first region mimics a lunar illumination effect based on a position of the moon. In embodiments, the method includes controlling a third light source to produce a melanopic flux ratio of at least 10:1 in a portion of the environment.

In embodiments, the first light source is disposed in the environment to produce cove lighting. In embodiments, the at least one of the first light source and the second light source is disposed in the environment to produce graze lighting. In embodiments, controlling the first light source and the second light source results in a melanopic flux ratio of at least 10:1 in a portion of the environment.

In embodiments, methods and systems of illumination in an environment, include controlling a downlight light source in the environment to mimic sky color for at least one of sunrise, mid-day sun, and sunset; and controlling an uplight light source in the environment in response to the downlight control so that the illumination in the environment produces a melanopic flux ratio of at least 10:1 in the environment.

In embodiments, control of the uplight light source includes adjusting at least two channels of a multiple channel light. In embodiments, controlling the uplight source further produces a circadian action. In embodiments, the method includes controlling the downlight light source to shift a bias of light in the environment toward at least a first side, a central portion, and a second side of the environment. In embodiments, the environment is a room. In embodiments, coordinating control of the uplight light source in the environment in response to the downlight control includes mimicking sky color for at least one of sunrise, mid-day sun, and sunset.

In embodiments, methods and systems include receiving biomarker information from a plurality of wearable sensors from at least one user in a lighting control environment over a time frame; recording control settings for at least one light in the lighting control environment over the time frame; and using machine learning to determine correlations between biological states of the user and lighting effects in the environment based on the biomarker information and the record of lighting control settings.

In embodiments, methods and systems include receiving estimated future time zone and an activity schedule of a user for a plurality of sequential days; identifying an estimated future time zone of the user that is different than a current time zone; and controlling at least one light proximal to the user according to the estimated different future time zone prior to the user being located in the different estimated time zone.

In embodiments, methods and systems include receiving architecture information for a space into a lighting design system; processing the architecture information with a three-dimensional physical space modeling algorithm that produces a model of the space including at least one of objects and surfaces in the space; and applying a model of luminance for a light fixture to the model of the space, thereby producing an emulation of luminance in the space including an impact of luminance from the light fixture on at least one of the objects and surfaces in the space.

In embodiments, the method includes lighting control system that uses machine learning to adapt light control settings for at least one light in an environment based on at least one of a schedule of time zones of a user in the environment, a schedule of activities of the user in the environment, biomarker information received from wearable sensors worn by at least one user in the environment, feedback on lighting effects caused by the light control settings from users in the environment, and user generated light control settings for at least one light in the environment.

In embodiments, methods and systems of configuring a three-dimensional space for lighting simulation include capturing information descriptive of physical aspects of an environment as a three-dimensional point-cloud representation of the physical aspects; applying machine learning to the descriptive information to detect architectural features of the environment; determining light modeling aspects of the detected architectural features; configuring a digital library of the architectural features including at least one of the light modeling aspects of each architectural feature in the library; and configuring a lighting space model of the environment that references the library of architectural features and incorporates corresponding light modeling aspects of architectural features referenced in the library.

In embodiments, capturing information descriptive of the physical aspects of an environment includes use of one or more of a digital camera, three-dimensional sensor, camera-equipped personal computing device capturing images of the environment. In embodiments, capturing information descriptive of the physical aspects of the environment includes generating measurements of the detected architectural features and distances between them. In embodiments, applying machine learning to the descriptive information includes processing point clouds of the environment. In embodiments, the method includes generating at least one of a floor plan and a reflected ceiling plan of the environment.

In embodiments, the method includes presenting the lighting space model in an artificial reality interface. In embodiments, the method includes detecting at least one light source in the environment. In embodiments, configuring a lighting space model incorporates light modeling aspects of the light source. In embodiments, configuring a lighting space model includes incorporating a light source model for at least one light source in the environment. In embodiments, the light modeling aspects of the detected architectural features include reflectance by the feature of light directed a first angle. In embodiments, the light modeling aspects of the detected architectural features include surface type for at least one surface of the feature.

In embodiments, methods and systems of configuring a three-dimensional space model for lighting simulation include capturing visual information representative of physical aspects of an environment as a three-dimensional visual representation of the environment; detecting at least one of surfaces and edges between surfaces in the visual representation; determining physical relationships among the detected surfaces and edges. In embodiments, the physical relationships include relative orientation of a plurality of the detected surfaces; analyzing an impact of illumination on at least one of the surfaces and the edges to generate a reflective model of the analyzed surfaces and edges; and configuring a lighting space model of the environment that incorporates the detected surfaces and edges, their orientations, and their reflective model.

In embodiments, capturing visual information representative of the physical aspects of the environment includes use of one or more of a digital camera, three-dimensional sensor, camera-equipped personal computing device to capture at least one image of a portion of the environment. In embodiments, determining physical relationships includes generating measurements of surfaces and distances between the surfaces. In embodiments, the method includes applying machine learning to an output of the analyzing an impact of illumination on at least one of the surfaces and the edges to improve generating the reflective model.

In embodiments, the method includes generating at least one of a floor plan and a reflected ceiling plan of the environment. In embodiments, the method includes presenting the lighting space model in an artificial reality interface. In embodiments, the method includes detecting at least one light source in the environment. In embodiments, configuring the three dimensional space model includes incorporating light modeling aspects of the light source.

In embodiments, configuring the three dimensional space model includes incorporating a light source model for at least one light source in the environment. In embodiments, the reflective model of the analyzed surfaces and edges includes reflectance by the surface of light directed a first angle. In embodiments, the reflective model of the analyzed surfaces and edges includes a surface type for at least one of the surfaces.

In embodiments, methods and systems include receiving from a server blockchain-secured digital image content representative of a low resolution of an environment; rendering, via processing the received content, a low resolution image of an impact of a light disposed at a first location and oriented in a first orientation in the environment on elements in the environment; and in response to receiving an indication, from a user interface on which the low resolution image is rendered, of a subset of the environment to render in high resolution, rendering in high resolution and transmitting a blockchain-secured digital image of the indicated subset of the environment.

In embodiments, rendering the low resolution version is performed by a mobile device and wherein the rendering in high resolution is performed by a networked server. In embodiments, the method includes displaying the transmitted high resolution digital image on a user interface of a mobile device receiving the blockchain-secured transmitted high resolution image. In embodiments, the digital image content representative of the subset includes a full geometric model of the indicated subset.

In embodiments, the digital image content representative of the subset includes a high resolution image of the impact of the light on the element in the environment. In embodiments, the low resolution version is rendered in a virtual reality display of the environment. In embodiments, the digital image content includes at least one of a near field illumination model of illumination produced by a light source and a far field illumination model of illumination produced by the light source.

In embodiments, the blockchain secures a low resolution image of a portion of the environment rendered on a mobile device and a high resolution image of the portion of the environment rendered on a computing server device. In embodiments, methods and systems include receiving a first blockchain-secured digital image content representative of an environment; rendering, via processing the received content, a first resolution version of an impact on elements in the environment of a light disposed at a first location and oriented in a first orientation in the environment; receiving subsequent blockchain-secured digital image content of the environment; rendering a subsequent resolution version of the impact of light on the elements by combining the received subsequent digital image content with the most recently rendered resolution version; and repeating the receiving subsequent and rendering a subsequent resolution version until at least a portion of the next resolution version includes a resolution equivalent to a high resolution version of the environment.

In embodiments, rendering the first resolution version is performed by a mobile device and wherein the rendering of at least one of the subsequent resolution versions is performed by a networked server and streamed to the mobile device. In embodiments, the digital image content of the environment includes a full geometric model of the indicated subset. In embodiments, the next resolution version of the impact of light on the elements includes a high resolution image. In embodiments, the first content is rendered in a virtual reality display of the environment.

In embodiments, methods and systems include receiving on a first computing device a first multi-dimensional image of illumination produced by a light source and captured with an indirect near field illumination multi-dimensional image capture device; rendering on a user interface of the first computing device a first resolution representation of the near field illumination; receiving additional position differentiated multi-dimensional images of illumination from the light source captured by the indirect near field capture device; and in response thereto, rendering increasingly higher resolution representations of the near field illumination.

In embodiments, a count of additional position differentiated multi-dimensional images received is limited based on a performance aspect of the first computing device. In embodiments, a count of additional position differentiated multi-dimensional images received and rendered by a mobile device is less than a count of additional position differentiated multi-dimensional images received and rendered by a server. In embodiments, rendering at least one of the increasingly higher solution representations of the nearfield illumination is performed by a computing device other than the first computing device.

In embodiments, receiving a first multi-dimensional image includes receiving a blockchain-secured message including the first multi-dimensional image. In embodiments, receiving additional multi-dimensional images includes receiving a blockchain-secured message including at least one of the additional multi-dimensional images.

In embodiments, methods and systems of producing a color tuning curve include controlling a first color control channel including setting an “x” value that corresponds to a first axis of a CIE diagram, based on a secondary control input; controlling a second color control channel including setting a “y” value that corresponds to a second axis of a CIE diagram, based on a secondary control input; controlling a third color control channel including setting a dim value that corresponds to a light output value; and controlling a fourth color control channel including setting at least one of the “x” value and the “y” value based on a primary control input.

In embodiments, the method includes producing the color tuning curve in an augmented reality lighting simulation environment. In embodiments, a lighting effect resulting from producing the color tuning curve is rendered throughout a three-dimensional space. In embodiments, the color tuning curve is applied to a lighting source in the three-dimensional space. In embodiments, the rendering includes accounting for effects relating to physical characteristics of light source. In embodiments, the rendering includes rendering distance-based light source intensity.

In embodiments, the rendering distance-based light source intensity includes rendering light source intensity fall-off over distance from the light source for each ray-trace in the set of ray-traces. In embodiments, the method includes providing a user interface that facilitates a user selecting a lighting fixture to control to produce the color tuning curve. In embodiments, the method includes providing a user interface that facilitate a user selecting at least one of a programmable dimming curve, programmable color tuning curve, a tuning curve start point, a tuning curve end point, a tuning curve dimming path, and a color tuning path.

In embodiments, in at least one of the turning curve dimming path and color tuning path is responsive to a level of dimming. In embodiments, the controlling steps are applied to a lighting system including three white light sources each with different CCTs to produce the color tuning curve. In embodiments, controlling steps are applied to a lighting system including a plurality of different color light emitting diode (LED) light sources to produce the color tuning curve. In embodiments, producing the color tuning curve is responsive to a user selection of a tuning curve start point, tuning curve end point and at least one tuning curve waypoint between the start and end points.

In embodiments, methods and systems for producing a single color of light across a plurality of color modes, include a four channel light emitting diode (LED) illumination source. In embodiments, each of the four channels are independently controllable for at least an amount of light output by the corresponding light emitting diode in the illumination source; a set of mathematical models that define features of each of a plurality of the color modes that, when processed with a map of LED illumination source channel control values for a plurality of target illumination colors by a processor produces a set of intensity information for each of the plurality of target illumination colors; and a computing architecture of the illumination source that receives an indication of a target color and a color mode and controls the four channels of the illumination source to produce the target color based on the set of intensity information and the indicated color mode.

In embodiments, a target color produced in power efficiency color mode is substantially the same color produced in a full power color mode. In embodiments, a common target color is produced by the system for each of a plurality of color modes consisting of a color quality mode, an efficacy mode, a circadian mode, a color bias mode, and a rest mode. In embodiments, a color quality mode is achieved by maximizing at least one of the color rendering index (CRI) and fidelity and gamut metrics.

In embodiments, an efficacy mode is achieved by maximizing output lumens per watt of consumed power. In embodiments, a circadian mode is achieved by maximizing equivalent melanopic lux (EML) content. In embodiments, a color bias mode is achieved by oversaturating a single color as a spectral component of a two-dimensionally indexed position on a color rendering index diagram. In embodiments, the rest mode is achieved by minimizing at least one of blue illumination and EML content.

In embodiments, the set of mathematical models facilitate producing a color tuning curve responsively to a user selection of a tuning curve start point, tuning curve end point and at least one tuning curve waypoint between the start and end points.

In embodiments, methods and systems of model-based rendering near-field effects of a light source, include modeling light source emissions as a set of direction-specific light ray-traces; capturing data at a plurality of positions along a portion of the set of direction-specific light ray-traces; determining interactions among the ray-traces; and rendering in an electronic display near-field effects of the light source, the effects derived from a lighting space model that incorporates the light volume-data, the interpolated plurality of points and the interactions among the ray-traces.

In embodiments, the lighting space model accounts for at least one of light transparency, absorption and reflection of elements in the three-dimensional space. In embodiments, the electronic display is controlled by a virtual reality display controller. In embodiments, the electronic display is an augmented reality display controlled by an augmented reality display controller. In embodiments, the rendering includes rendering near-field lighting artifacts. In embodiments, the near-field lighting artifacts are rendered throughout the three-dimensional space. In embodiments, the rendering includes accounting for effects relating to physical characteristics of a source of the light emissions. In embodiments, a light source of the light emissions includes a plurality of distinct light elements, each distinct light element being associated with a corresponding set of ray-traces.

In embodiments, the rendering includes rendering effects from each of the plurality of distinct light elements. In embodiments, the rendering includes rendering distance-based light source intensity. In embodiments, the rendering distance-based light source intensity includes rendering light source intensity fall-off over distance from the light source for each ray-trace in the set of ray-traces.

In embodiments, storing the captured data in a computer accessible memory as three-dimensional light volume-data and interpolating light source emissions for a plurality of points in a three-dimensional space characterized by the three-dimensional light volume data that are not present in the light volume-data.

In embodiments, the three-dimensional light volume data includes a shape of a lighting effect from a light source of the light emissions. In embodiments, a lighting effect property includes a shape of the lighting effect at a specified distance from the light source. In embodiments, the shape is a substantially continuous shape. In embodiments, the shape is a discontinuous pattern. In embodiments, the near-field data effects of the light source include at least one of a color and an intensity of a lighting effect. In embodiments, the near field data effects of the light source include a reflection from a surface.

In embodiments, methods and systems of the model-based rendering of a light field, include capturing a set of data representing a three-dimensional space proximal to a light source, the data set including data representing illuminance values of light at each of a plurality of locations in the three-dimensional space; extracting a multi-dimensional portion of the set of data; and generating a geometric model of the portion that facilitates modelling an impact of the illuminance of the light source on objects disposed in the space proximal to the light source.

In embodiments, the methods include interpolating a plurality of additional illuminance values within the multi-dimensional portion of the set of data. In embodiments, the three-dimensional data set includes a reflection from a surface. In embodiments, methods and systems for planning lighting in an augmented reality display, include representing physical features of an environment as a point cloud; using machine learning to generate a lighting space model of the environment from the point cloud; using the lighting space model to produce at least one of a floor plan and a reflected ceiling plan of the environment; coupling the lighting space model to an augmented reality view of the environment. In embodiments, light sources are added to the lighting space model by a user placing light sources in the augmented reality view of the environment; and rendering the environment through the augmented reality display including lighting effects of the placed light sources based on a near field illumination characterization of the placed light sources.

In embodiments, the rendering includes accounting for at least one of light transparency, absorption and reflection of at least one element in the environment. In embodiments, the rendering includes rendering near-field lighting artifacts. In embodiments, the near-field lighting artifacts are rendered throughout the three-dimensional space. In embodiments, the rendering includes accounting for effects relating to physical characteristics of at least one light source.

In embodiments, the at least one light source includes a plurality of distinct light elements, each distinct light element being associated with a corresponding set of ray-traces. In embodiments, the rendering includes rendering effects from each of the plurality of distinct light elements. In embodiments, the lighting effects of the placed light sources are based on an area-source model of the placed light sources. In embodiments, placing light sources includes selecting a model of a light source from a catalog of light sources presented in the augmented reality environment and indicating at least one of a position and orientation of the light source in the environment. In embodiments, the rendering is performed by a volumetric renderer.

In embodiments, methods and systems for planning lighting in an augmented reality display, include representing physical features of an environment as surfaces and edges; using machine learning to generate a lighting space model of the environment from the surfaces and edges. In embodiments, the lighting space model includes attributes for at least one of absorption and reflectance of each of the surfaces; using the lighting space model to produce a floor plan of the environment; coupling the lighting space model to an augmented reality view of the environment. In embodiments, light sources are added to the lighting space model and to the produced floor plan by a user placing light sources in the augmented reality view of the environment; and rendering the environment through the augmented reality display including lighting effects of the placed light sources based on a near field illumination characterization of the placed light sources.

In embodiments, the rendering includes accounting for at least one of light transparency, absorption and reflection of at least one surface in the environment. In embodiments, the rendering includes rendering near-field lighting artifacts. In embodiments, the near-field lighting artifacts are rendered throughout the three-dimensional space. In embodiments, the rendering includes accounting for effects relating to physical characteristics of at least one light source. In embodiments, the at least one light source includes a plurality of distinct light elements, each distinct light element being associated with a corresponding set of ray-traces. In embodiments, the rendering includes rendering effects from each of the plurality of distinct light elements.

In embodiments, the lighting effects of the placed light sources are based on an area-source model of the placed light sources. In embodiments, placing light sources includes selecting a model of a light source from a catalog of light sources presented in the augmented reality environment and indicating at least one of a position and orientation of the light source in the environment. In embodiments, the rendering is performed by a volumetric renderer. In embodiments, producing a floor plan includes use of a measuring facility in the AR interface to measure a space portrayed therein.

In embodiments, methods and systems for planning lighting in an augmented reality display, include representing physical features of an environment as a point cloud; using machine learning to generate a lighting space model of the environment from the point cloud; using the lighting space model to produce at least one of a floor plan and a reflected ceiling plan of the environment; coupling the lighting space model to an augmented reality view of the environment. In embodiments, light sources are added to the lighting space model by a user placing light sources in the augmented reality view of the environment by selecting a model of a light source from a catalog of light sources presented in the augmented reality environment and indicating at least one of a position and orientation of the light source in the environment; rendering the environment through the augmented reality display including lighting effects of the placed light sources based on a near field illumination characterization of the placed light sources; and populating a data object with item identification information for at least one of the placed light sources. In embodiments, populating causes automatic placement of at least one order into a supply chain for the at least one placed light source.

In embodiments, the rendering includes rendering near-field lighting effects in the environment of the placed light sources based on a near-file illumination model of the light source. In embodiments, populating includes obtaining the item identification information from the catalog of light sources. In embodiments, populating is in response to a user indicating in the augmented reality interface the at least one of the placed lights for automatic order placement. In embodiments, the methods include producing a lighting installation plan based on the floor plan and the position and orientation of the light source in the environment.

In embodiments, the method includes populating an automatic procurement data object with item identification information for at least one object identified in the floor plan. In embodiments, the rendering includes accounting for at least one of light transparency, absorption and reflection of at least one element in the environment. In embodiments, the rendering includes rendering near-field lighting artifacts. In embodiments, the near-field lighting artifacts are rendered throughout the three-dimensional space. In embodiments, the rendering includes accounting for effects relating to physical characteristics of at least one light source.

In embodiments, the at least one light source includes a plurality of distinct light elements, each distinct light element being associated with a corresponding set of ray-traces. In embodiments, the rendering includes rendering effects from each of the plurality of distinct light elements. In embodiments, the lighting effects of the placed light sources are based on an area-source model of the placed light sources. In embodiments, the catalog of light sources includes automated order light source identification information. In embodiments, the rendering is performed by a volumetric renderer.

In embodiments, methods and systems for planning lighting in an augmented reality display, include representing physical features of an environment as surfaces and edges; using machine learning to generate a lighting space model of the environment from the surfaces and edges. In embodiments, the lighting space model includes attributes for at least one of absorption and reflectance of each of the surfaces; using the lighting space model to produce a floor plan of the environment; coupling the lighting space model to an augmented reality view of the environment. In embodiments, light sources are added to the lighting space model and to the produced floor plan by a user placing light sources in the augmented reality view of the environment; rendering the environment through the augmented reality display including lighting effects of the placed light sources based on a near field illumination characterization of the placed light sources; and populating a data object with item identification information for at least one of the placed light sources. In embodiments, populating causes automatic placement of at least one order into a supply chain for the at least one placed light source.

In embodiments, the rendering includes rendering near-field lighting effects in the environment of the placed light sources based on a near-file illumination model of the light source. In embodiments, populating includes obtaining the item identification information from the catalog of light sources. In embodiments, populating is in response to a user indicating in the augmented reality interface the at least one of the placed lights for automatic order placement. In embodiments, the method includes producing a lighting installation plan based on the floor plan and the position and orientation of the light source in the environment. In embodiments, the method includes populating an automatic procurement data object with item identification information for at least one object identified in the floor plan. In embodiments, the rendering includes accounting for at least one of light transparency, absorption and reflection of at least one surface in the environment. In embodiments, the rendering includes rendering near-field lighting artifacts.

In embodiments, the near-field lighting artifacts are rendered throughout the three-dimensional space. In embodiments, the rendering includes accounting for effects relating to physical characteristics of at least one light source. In embodiments, the at least one light source includes a plurality of distinct light elements, each distinct light element being associated with a corresponding set of ray-traces. In embodiments, the rendering includes rendering effects from each of the plurality of distinct light elements. In embodiments, the lighting effects of the placed light sources are based on an area-source model of the placed light sources. In embodiments, the rendering is performed by a volumetric renderer.

In embodiments, methods and systems of control of modeled light sources in an augmented reality interface, include coupling a lighting space model of an environment to an augmented reality view of the environment. In embodiments, light sources are added to the lighting space model by a user placing light sources in the augmented reality view of the environment; rendering the environment through the augmented reality display including lighting effects of the placed light sources based on a near field illumination characterization of the placed light sources; and configuring a plurality of virtual lighting controls in the augmented reality user interface that control illumination from at least one of the placed light sources.

In embodiments, the plurality of virtual lighting controls includes user interface elements for controlling at least one of dimming level, fixture color, fixture finish, beam angles, light intensity, light color, and light color temperature. In embodiments, the plurality of virtual lighting controls includes user interface elements for controlling at least one of rotation, placement, orientation, and tilt of the placed light sources. In embodiments, the method includes interfacing the virtual lighting controls to wearable sensors that indicate a motion of a portion of a user wearing the wearable sensors.

In embodiments, the indication of motion from the wearable sensors is interpreted by the plurality of virtual lighting controls to control at least one of light intensity, light color, and light color temperature. In embodiments, the indication of motion from the wearable sensors is interpreted by the plurality of virtual lighting controls to control at least one of rotation and tilt of the placed light sources. In embodiments, the method includes rendering a marketplace of light sources in a portion of the augmented reality display from which a user selects the light sources to be added to the lighting space model.

In embodiments, methods and systems of control of modeled light sources in an augmented reality interface, include coupling a lighting space model of an environment to an augmented reality view of the environment. In embodiments, light sources are added to the lighting space model by a user placing light sources in the augmented reality view of the environment; rendering the environment through the augmented reality display including lighting effects of the placed light sources based on a near field illumination characterization of the placed light sources; and configuring a plurality of virtual lighting controls in a user interface of a handheld portable computing device that control at least one of the placed light sources.

In embodiments, the plurality of virtual lighting controls includes user interface elements for controlling at least one of light intensity, light color, and light color temperature. In embodiments, the plurality of virtual lighting controls includes user interface elements for controlling at least one of rotation and tilt of the placed light sources. In embodiments, the method includes interfacing the virtual lighting controls to wearable sensors that indicate a motion of a portion of a user wearing the wearable sensors. In embodiments, the indication of motion from the wearable sensors is interpreted by the plurality of virtual lighting controls to control at least one of light intensity, light color, and light color temperature. In embodiments, the indication of motion from the wearable sensors is interpreted by the plurality of virtual lighting controls to control at least one of rotation and tilt of the placed light sources.

In embodiments, the method includes rendering a marketplace of light sources in a portion of the augmented reality display from which a user selects the light sources to be added to the lighting space model. In embodiments, the virtual lighting controls include a touchable element on an electronic tablet display. In embodiments, the virtual lighting controls include an adjustable dial that represents a range of filter effects producible by the light. In embodiments, methods and systems of control of modeled light sources in an augmented reality interface, include coupling a lighting space model of an environment to an augmented reality view of the environment. In embodiments, light sources are added to the lighting space model by a user placing light sources in the augmented reality view of the environment; rendering the environment through the augmented reality display including lighting effects of the placed light sources based on a near field illumination characterization of the placed light sources; and configuring the lighting space model to receive input from a handheld portable computing device that control at least one of the placed light sources.

In embodiments, the input from the handheld portable computing device includes data that indicates at least one of an orientation and a movement of the handheld portable computing device. In embodiments, the orientation input indicates a new orientation in the environment for the at least one of the placed light sources. In embodiments, the movement input indicates a new position in the environment for the at least one of the placed light sources.

