We propose a framework called inverse augmented reality (IAR) which describes the scenario that a virtual agent living in the virtual world can observe both virtual objects and real objects. This is different from the traditional augmented reality. The traditional virtual reality, mixed reality and augmented reality are all generated for humans, i.e., they are human-centered frameworks. On the contrary, the proposed inverse augmented reality is a virtual agent-centered framework, which represents and analyzes the reality from a virtual agent's perspective. In this paper, we elaborate the framework of inverse augmented reality to argue the equivalence of the virtual world and the physical world regarding the whole physical structure.
In the past, a lot of related researches about augmented reality have been presented. Before IAR, some novel styles of reality have been proposed. For example, Lifton al @cite_4 proposed the dual reality'' system to make the virtual world and the physical world be corresponding to each other. Roo al @cite_0 proposed the one reality'' system, which contained a 6-level mixture of virtual and real contents ranging from purely physical to purely virtual world. But they all describe the mixed reality from the perspective of humans, ignoring the view from the virtual world.
@cite_0 @cite_4
2744963411 2043470575 1482795035 2584852980
Most of our daily activities take place in the physical world, which inherently imposes physical constraints. In contrast, the digital world is very flexible, but usually isolated from its physical counterpart. To combine these two realms, many Mixed Reality (MR) techniques have been explored, at different levels in the continuum. In this work we present an integrated Mixed Reality ecosystem that allows users to incrementally transition from pure physical to pure virtual experiences in a unique reality. This system stands on a conceptual framework composed of 6 levels. This paper presents these levels as well as the related interaction techniques. We present a method for estimating the real-world lighting conditions within a scene in real-time. The estimation is based on the visual appearance of a human face in the real scene captured in a single image of a monocular camera. In hardware setups featuring a user-facing camera, an image of the user’s face can be acquired at any time. The limited range in variations between different human faces makes it possible to analyze their appearance offline, and to apply the results to new faces. Our approach uses radiance transfer functions – learned offline from a dataset of images of faces under different known illuminations – for particular points on the human face. Based on these functions, we recover the most plausible real-world lighting conditions for measured reflections in a face, represented by a function depending on incident light angle using Spherical Harmonics. The pose of the camera relative to the face is determined by means of optical tracking, and virtual 3D content is rendered and overlaid onto the real scene with a fixed spatial relationship to the face. By applying the estimated lighting conditions to the rendering of the virtual content, the augmented scene is shaded coherently with regard to the real and virtual parts of the scene. We show with different examples under a variety of lighting conditions, that our approach provides plausible results, which considerably enhance the visual realism in real-time Augmented Reality applications. Like virtual reality, augmented reality is becoming an emerging platform in new application areas for museums, edutainment, home entertainment, research, industry, and the art communities using novel approaches which have taken augmented reality beyond traditional eye-worn or hand-held displays. In this book, the authors discuss spatial augmented reality approaches that exploit optical elements, video projectors, holograms, radio frequency tags, and tracking technology, as well as interactive rendering algorithms and calibration techniques in order to embed synthetic supplements into the real environment or into a live video of the real environment. Special Features: - Comprehensive overview - Detailed mathematical equations - Code fragments - Implementation instructions - Examples of Spatial AR displays The authors have put together a preliminary collection of Errata. Updates will be posted to this site as necessary. Augmented reality combines real footage taken of a scene with virtual elements. However, most current methods rely on camera localisation and 3D reconstruction or point cloud generation in order to integrate augmented reality to the footage. In contrast, in this work we present a novel method to augment virtual reality to the scene based on the recognition of dominant planes in interior scenes. Our method uses a rule system to select predefined decisions on a set of variables in order to infer dominant planes in the scene. For this, we propose to combine information from texture features, a measure of blurring in the dominant planes, and a segmentation of regions in a scene based on superpixels. The rule system infers the regions corresponding to the dominant planes, whilst light intensity in the scene is inferred from segmented regions. We also propose an approach to remove regions misclassified as dominant planes. Finally, the floor in the scene is recognised as the most dominant plane and then replaced with an augmented texture. We demonstrate our approach in a video sequence where our method is applied in a frame-to-frame basis, thus, for each single image in the video sequence, the floor is automatically recognised as the most dominant plane and then replaced with a virtual texture and furthermore, whose appearance is modified according to our coarse light model inferred from our approach.