In this paper we present a framework for the rendering of dynamic 3D virtual environments which can be integrated in the development of videogames. It includes methods to manage sounds and particle effects, paged static geometries, the support of a physics engine and various input systems. It has been designed with a modular structure to allow future expansions. We exploited some open-source state-of-the-art components such as OGRE, PhysX, ParticleUniverse, etc.; all of them have been properly integrated to obtain peculiar physical and environmental effects. The stand-alone version of the application is fully compatible with Direct3D and OpenGL APIs and adopts OpenAL APIs to manage audio cards. Concluding, we devised a showcase demo which reproduces a dynamic 3D environment, including some particular effects: the alternation of day and night influencing the lighting of the scene, the rendering of terrain, water and vegetation, the reproduction of sounds and atmospheric agents.
An overview of Halo 3 's unique lighting and material system and its main components is treated in @cite_4 . Halo includes key innovations in the following areas: spherical harmonics lightmap generation, compression and rendering; rendering complex materials under area light sources; HDR rendering and post-processing. Some of the effects presented in that work have been adopted also here (e.g. the HDR rendering).
@cite_4
2017214388
This paper focuses on efficient rendering based on pre-computed light transport, with realistic materials and shadows under all-frequency direct lighting such an environment maps. The basic difficulty is representation and computation in the 6D space of light direction, view direction, and surface position. While image-based and synthetic methods for real-time rendering have been proposed, they do not scale to high sampling rates with variation of both lighting and viewpoint. Current approaches are therefore limited to lower dimensionality (only lighting or viewpoint variation, not both) or lower sampling rates (low frequency lighting and materials). We propose a new mathematical and computational analysis of pre-computed light transport. We use factored forms, separately pre-computing and representing visibility and material properties. Rendering then requires computing triple product integrals at each vertex, involving the lighting, visibility and BRDF. Our main contribution is a general analysis of these triple product integrals, which are likely to have broad applicability in computer graphics and numerical analysis. We first determine the computational complexity in a number of bases like point samples, spherical harmonics and wavelets. We then give efficient linear and sublinear-time algorithms for Haar wavelets, incorporating non-linear wavelet approximation of lighting and BRDFs. Practically, we demonstrate rendering of images under new lighting and viewing conditions in a few seconds, significantly faster than previous techniques. We present a method, based on pre-computed light transport, for real-time rendering of objects under all-frequency, time-varying illumination represented as a high-resolution environment map. Current techniques are limited to small area lights, with sharp shadows, or large low-frequency lights, with very soft shadows. Our main contribution is to approximate the environment map in a wavelet basis, keeping only the largest terms (this is known as a non-linear approximation). We obtain further compression by encoding the light transport matrix sparsely but accurately in the same basis. Rendering is performed by multiplying a sparse light vector by a sparse transport matrix, which is very fast. For accurate rendering, using non-linear wavelets is an order of magnitude faster than using linear spherical harmonics, the current best technique. In this paper we are extracting surface reflectance and natural environmental illumination from a reflectance map, i.e. from a single 2D image of a sphere of one material under one illumination. This is a notoriously difficult problem, yet key to various re-rendering applications. With the recent advances in estimating reflectance maps from 2D images their further decomposition has become increasingly relevant. To this end, we propose a Convolutional Neural Network (CNN) architecture to reconstruct both material parameters (i.e. Phong) as well as illumination (i.e. high-resolution spherical illumination maps), that is solely trained on synthetic data. We demonstrate that decomposition of synthetic as well as real photographs of reflectance maps, both in High Dynamic Range (HDR), and, for the first time, on Low Dynamic Range (LDR) as well. Results are compared to previous approaches quantitatively as well as qualitatively in terms of re-renderings where illumination, material, view or shape are changed. Visualizing changes to indoor scenes is important for many applications. When looking for a new place to live, we want to see how the interior looks not with the current inhabitant's belongings, but with our own furniture. Before purchasing a new sofa, we want to visualize how it would look in our living room. In this paper, we present a system that takes an RGBD scan of an indoor scene and produces a scene model of the empty room, including light emitters, materials, and the geometry of the non-cluttered room. Our system enables realistic rendering not only of the empty room under the original lighting conditions, but also with various scene edits, including adding furniture, changing the material properties of the walls, and relighting. These types of scene edits enable many mixed reality applications in areas such as real estate, furniture retail, and interior design. Our system contains two novel technical contributions: a 3D radiometric calibration process that recovers the appearance of the scene in high dynamic range, and a global-illumination-aware inverse rendering framework that simultaneously recovers reflectance properties of scene surfaces and lighting properties for several light source types, including generalized point and line lights. Gradient-based methods are becoming increasingly important for computer graphics, machine learning, and computer vision. The ability to compute gradients is crucial to optimization, inverse problems, and deep learning. In rendering, the gradient is required with respect to variables such as camera parameters, light sources, scene geometry, or material appearance. However, computing the gradient of rendering is challenging because the rendering integral includes visibility terms that are not differentiable. Previous work on differentiable rendering has focused on approximate solutions. They often do not handle secondary effects such as shadows or global illumination, or they do not provide the gradient with respect to variables other than pixel coordinates. We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters. The key to our method is a novel edge sampling algorithm that directly samples the Dirac delta functions introduced by the derivatives of the discontinuous integrand. We also develop efficient importance sampling methods based on spatial hierarchies. Our method can generate gradients in times running from seconds to minutes depending on scene complexity and desired precision. We interface our differentiable ray tracer with the deep learning library PyTorch and show prototype applications in inverse rendering and the generation of adversarial examples for neural networks. We compress storage and accelerate performance of precomputed radiance transfer (PRT), which captures the way an object shadows, scatters, and reflects light. PRT records over many surface points a transfer matrix. At run-time, this matrix transforms a vector of spherical harmonic coefficients representing distant, low-frequency source lighting into exiting radiance. Per-point transfer matrices form a high-dimensional surface signal that we compress using clustered principal component analysis (CPCA), which partitions many samples into fewer clusters each approximating the signal as an affine subspace. CPCA thus reduces the high-dimensional transfer signal to a low-dimensional set of per-point weights on a per-cluster set of representative matrices. Rather than computing a weighted sum of representatives and applying this result to the lighting, we apply the representatives to the lighting per-cluster (on the CPU) and weight these results per-point (on the GPU). Since the output of the matrix is lower-dimensional than the matrix itself, this reduces computation. We also increase the accuracy of encoded radiance functions with a new least-squares optimal projection of spherical harmonics onto the hemisphere. We describe an implementation on graphics hardware that performs real-time rendering of glossy objects with dynamic self-shadowing and interreflection without fixing the view or light as in previous work. Our approach also allows significantly increased lighting frequency when rendering diffuse objects and includes subsurface scattering. Acquiring High Dynamic Range (HDR) light-fields from several images with different exposures (sensor integration periods) has been widely considered for static camera positions. In this paper a new approach is proposed that enables 3D HDR environment maps to be acquired directly from a dynamic set of images in real-time. In particular a method will be proposed to use an RGB-D camera as a dynamic light-field sensor, based on a dense real-time 3D tracking and mapping approach, that avoids the need for a light-probe or the observation of reflective surfaces. The 6dof pose and dense scene structure will be estimated simultaneously with the observed dynamic range so as to compute the radiance map of the scene and fuse a stream of low dynamic range images (LDR) into an HDR image. This will then be used to create an arbitrary number of virtual omni-directional light-probes that will be placed at the positions where virtual augmented objects will be rendered. In addition, a solution is provided for the problem of automatic shutter variations in visual SLAM. Augmented reality results are provided which demonstrate real-time 3D HDR mapping, virtual light-probe synthesis and light source detection for rendering reflective objects with shadows seamlessly with the real video stream in real-time. Previous work has demonstrated that the image variation of many objects (human faces in particular) under variable lighting can be effectively modeled by low-dimensional linear spaces, even when there are multiple light sources and shadowing. Basis images spanning this space are usually obtained in one of three ways: a large set of images of the object under different lighting conditions is acquired, and principal component analysis (PCA) is used to estimate a subspace. Alternatively, synthetic images are rendered from a 3D model (perhaps reconstructed from images) under point sources and, again, PCA is used to estimate a subspace. Finally, images rendered from a 3D model under diffuse lighting based on spherical harmonics are directly used as basis images. In this paper, we show how to arrange physical lighting so that the acquired images of each object can be directly used as the basis vectors of a low-dimensional linear space and that this subspace is close to those acquired by the other methods. More specifically, there exist configurations of k point light source directions, with k typically ranging from 5 to 9, such that, by taking k images of an object under these single sources, the resulting subspace is an effective representation for recognition under a wide range of lighting conditions. Since the subspace is generated directly from real images, potentially complex and or brittle intermediate steps such as 3D reconstruction can be completely avoided; nor is it necessary to acquire large numbers of training images or to physically construct complex diffuse (harmonic) light fields. We validate the use of subspaces constructed in this fashion within the context of face recognition. In this chapter we will cover approaches for creating visually complex, rich interactive environments as a case study of developing the world of ATI "ToyShop" demo. We will discuss the constraints for developing large immersive worlds in real-time, and go over the considerations for developing lighting environments for such scene rendering. Rain-specific effects in city environments will be presented. We will overview the lightning system used to create illumination from the lightning flashes, the high dynamic range rendering techniques used, various approaches for rendering rain effects and dynamic water simulation on the GPU. Methods for rendering reflections in real-time will be illustrated. Additionally, a number of specific material shaders for enhancing the feel of the rainy urban environment will be examined. High dynamic range (HDR) imaging is an area of increasing importance, but most display devices still have limited dynamic range (LDR). Various techniques have been proposed for compressing the dynamic range while retaining important visual information. Multi-scale image processing techniques, which are widely used for many image processing tasks, have a reputation of causing halo artifacts when used for range compression. However, we demonstrate that they can work when properly implemented. We use a symmetrical analysis-synthesis filter bank, and apply local gain control to the subbands. We also show that the technique can be adapted for the related problem of "companding", in which an HDR image is converted to an LDR image, and later expanded back to high dynamic range. We consider the rendering of diffuse objects under distant illumination, as specified by an environment map. Using an analytic expression for the irradiance in terms of spherical harmonic coefficients of the lighting, we show that one needs to compute and use only 9 coefficients, corresponding to the lowest-frequency modes of the illumination, in order to achieve average errors of only 1 . In other words, the irradiance is insensitive to high frequencies in the lighting, and is well approximated using only 9 parameters. In fact, we show that the irradiance can be procedurally represented simply as a quadratic polynomial in the cartesian components of the surface normal, and give explicit formulae. These observations lead to a simple and efficient procedural rendering algorithm amenable to hardware implementation, a prefiltering method up to three orders of magnitude faster than previous techniques, and new representations for lighting design and image-based rendering. Lighting and material are very important aspects of the visual appearances of games and they present some of the hardest challenges in real time graphics today. For Halo and indeed many other games, keeping the players immersed in the virtual environment for long periods of time is a top priority of the graphics system, and good quality lighting and realistic materials are the fundamental building blocks for achieving the level of realism necessary to accomplish this goal. Real-time shading with environment maps requires the ability to rotate the global lighting to each surface point's local coordinate frame. Although extensive previous work has studied rotation of functions represented by spherical harmonics, little work has investigated efficient rotation of wavelets. Wavelets are superior at approximating high frequency signals such as detailed high dynamic range lighting and very shiny BRDFs, but present difficulties for interactive rendering due to the lack of an analytic solution for rotation. In this paper we present an efficient computational solution for wavelet rotation using precomputed matrices. Each matrix represents the linear transformation of source wavelet bases defined in the global coordinate frame to target wavelet bases defined in sampled local frames. Since wavelets have compact support, these matrices are very sparse, enabling efficient storage and fast computation at run-time. In this paper, we focus on the application of our technique to interactive environment map rendering. We show that using these matrices allows us to evaluate the integral of dynamic lighting with dynamic BRDFs at interactive rates, incorporating efficient non-linear approximation of both illumination and reflection. Our technique improves on previous work by eliminating the need for prefiltering environment maps, and is thus significantly faster for accurate rendering of dynamic environment lighting with high frequency reflection effects. We generalize Richardson-Lucy deblurring to 4-D light fields by replacing the convolution steps with light field rendering of motion blur. The method deals correctly with blur caused by 6-degree-of-freedom camera motion in complex 3-D scenes, without performing depth estimation. We include a novel regularization term that maintains parallax information in the light field, and employ 4-D anisotropic total variation to reduce noise and ringing. We demonstrate the method operating effectively on rendered scenes and scenes captured using an off-the-shelf light field camera mounted on an industrial robot arm. Examples include complex 3-D geometry and cover all major classes of camera motion. Both qualitative and quantitative results confirm the effectiveness of the method over a range of conditions, including commonly occurring cases for which previously published methods fail. We include mathematical proof that the algorithm converges to the maximum-likelihood estimate of the unblurred scene under Poisson noise. Image-based or light field rendering has received much recent attention as an alternative to traditional geometric methods for modeling and rendering complex objects. A light field represents the radiance flowing through all the points in a scene in all possible directions. We explore two new techniques for efficiently acquiring, storing, and reconstructing light fields in a (nearly) uniform fashion. Both techniques sample the light field by sampling the set of lines that intersect a sphere tightly fit around a given object. Our first approach relies on uniformly subdividing the sphere and representing this subdivision in a compact data structure which allows efficient mapping of image pixels or rays to sphere points and then to subdivision elements. We sample a light field by joining pairs of subdivision elements and store the resulting samples in a multi-resolution, highly compressed fashion that allows efficient rendering. Our second method allows a uniform sampling of all five dimensions of the light field, using hierarchical subdivision for directional space and uniform grid sampling for positional space. Light field models are acquired using parallel projections along a set of uniform directions. Depth information can also be stored for high-quality image rendering. The system can provide bounds on key sources of error in the representation and can be generalized to arbitrary scenes comprising multiple complex objects. We consider real-time rendering of scenes in participating media, capturing the effects of light scattering in fog, mist and haze. While a number of sophisticated approaches based on Monte Carlo and finite element simulation have been developed, those methods do not work at interactive rates. The most common real-time methods are essentially simple variants of the OpenGL fog model. While easy to use and specify, that model excludes many important qualitative effects like glows around light sources, the impact of volumetric scattering on the appearance of surfaces such as the diffusing of glossy highlights, and the appearance under complex lighting such as environment maps. In this paper, we present an alternative physically based approach that captures these effects while maintaining real time performance and the ease-of-use of the OpenGL fog model. Our method is based on an explicit analytic integration of the single scattering light transport equations for an isotropic point light source in a homogeneous participating medium. We can implement the model in modern programmable graphics hardware using a few small numerical lookup tables stored as texture maps. Our model can also be easily adapted to generate the appearances of materials with arbitrary BRDFs, environment map lighting, and precomputed radiance transfer methods, in the presence of participating media. Hence, our techniques can be widely used in real-time rendering. Physically based rendering is a well-understood technique to produce realistic-looking images. However, different algorithms exist for efficiency reasons, which work well in certain cases but fail or produce rendering artefacts in others. Few tools allow a user to gain insight into the algorithmic processes. In this work, we present such a tool, which combines techniques from information visualization and visual analytics with physically based rendering. It consists of an interactive parallel coordinates plot, with a built-in sampling-based data reduction technique to visualize the attributes associated with each light sample. Twodimensional (2D) and three-dimensional (3D) heat maps depict any desired property of the rendering process. An interactively rendered 3D view of the scene displays animated light paths based on the user’s selection to gain further insight into the rendering process. The provided interactivity enables the user to guide the rendering process for more efficiency. To show its usefulness, we present several applications based on our tool. This includes differential light transport visualization to optimize light setup in a scene, finding the causes of and resolving rendering artefacts, such as fireflies, as well as a path length contribution histogram to evaluate the efficiency of different Monte Carlo estimators. Real-time shading using general (e.g., anisotropic) BRDFs has so far been limited to a few point or directional light sources. We extend such shading to smooth, area lighting using a low-order spherical harmonic basis for the lighting environment. We represent the 4D product function of BRDF times the cosine factor (dot product of the incident lighting and surface normal vectors) as a 2D table of spherical harmonic coefficients. Each table entry represents, for a single view direction, the integral of this product function times lighting on the hemisphere expressed in spherical harmonics. This reduces the shading integral to a simple dot product of 25 component vectors, easily evaluatable on PC graphics hardware. Non-trivial BRDF models require rotating the lighting coefficients to a local frame at each point on an object, currently forming the computational bottleneck. Real-time results can be achieved by fixing the view to allow dynamic lighting or vice versa. We also generalize a previous method for precomputed radiance transfer to handle general BRDF shading. This provides shadows and interreflections that respond in real-time to lighting changes on a preprocessed object of arbitrary material (BRDF) type. Volumetric data commonly has high depth complexity which makes it difficult to judge spatial relationships accurately. There are many different ways to enhance depth perception, such as shading, contours, and shadows. Artists and illustrators frequently employ halos for this purpose. In this technique, regions surrounding the edges of certain structures are darkened or brightened which makes it easier to judge occlusion. Based on this concept, we present a flexible method for enhancing and highlighting structures of interest using GPU-based direct volume rendering. Our approach uses an interactively defined halo transfer function to classify structures of interest based on data value, direction, and position. A feature-preserving spreading algorithm is applied to distribute seed values to neighboring locations, generating a controllably smooth field of halo intensities. These halo intensities are then mapped to colors and opacities using a halo profile function. Our method can be used to annotate features at interactive frame rates. We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous work that relies on specialized image capture, user input, and or simple scene models, we train an end-to-end deep neural network that directly regresses a limited field-of-view photo to HDR illumination, without strong assumptions on scene geometry, material properties, or lighting. We show that this can be accomplished in a three step process: 1) we train a robust lighting classifier to automatically annotate the location of light sources in a large dataset of LDR environment maps, 2) we use these annotations to train a deep neural network that predicts the location of lights in a scene from a single limited field-of-view photo, and 3) we fine-tune this network using a small dataset of HDR environment maps to predict light intensities. This allows us to automatically recover high-quality HDR illumination estimates that significantly outperform previous state-of-the-art methods. Consequently, using our illumination estimates for applications like 3D object insertion, produces photo-realistic results that we validate via a perceptual user study.