In this paper we consider the problem of computing the 3D shape of an unknown, arbitrarily-shaped scene from multiple photographs taken at known but arbitrarilydistributed viewpoints. By studying the equivalence class of all 3D shapes that reproduce the input photographs, we prove the existence of a special member of this class, the photo hull, that (1) can be computed directly from photographs of the scene, and (2) subsumes all other members of this class. We then give a provably-correct algorithm, called Space Carving, for computing this shape and present experimental results on complex real-world scenes. The approach is designed to (1) build photorealistic shapes that accurately model scene appearance from a wide range of viewpoints, and (2) account for the complex interactions between occlusion, parallax, shading, and their effects on arbitrary views of a 3D scene. 1.

A novel scene reconstruction technique is presented, different from previous approaches in its ability to cope with large changes in visibility and its modeling of intrinsic scene color and texture information. The method avoids image correspondence problems by working in a discretized scene space whose voxels are traversed in a fixed visibility ordering. This strategy takes full account of occlusions and allows the input cameras to be far apart and widely distributed about the environment. The algorithm identifies a special set of invariant voxels which together form a spatial and photometric reconstruction of the scene, fully consistent with the input images.

A comprehensive survey of computer vision-based human motion capture literature from the past two decades is presented. The focus is on a general overview based on a taxonomy of system functionalities, broken down into four processes: initialization, tracking, pose estimation, and recognition. Each process is discussed and divided into subprocesses and/or categories of methods to provide a reference to describe and compare the more than 130 publications covered by the survey. References are included throughout the paper to exemplify important issues and their relations to the various methods. A number of general assumptions used in this research field are identified and the character of these assumptions indicates that the research field is still in an early stage of development. To evaluate the state of the art, the major application areas are identified and performances are analyzed in light of the methods

In this paper, we describe an efficient image-based approach to computing and shading visual hulls from silhouette image data. Our algorithm takes advantage of epipolar geometry and incremental computation to achieve a constant rendering cost per rendered pixel. It does not suffer from the computation complexity, limited resolution, or quantization artifacts of previous volumetric approaches. We demonstrate the use of this algorithm in a real-time virtualized reality application running off a small number of video streams.

. We present new algorithms for creating and rendering visual hulls in real-time. Unlike voxel or sampled approaches, we compute an exact polyhedral representation for the visual hull directly from the silhouettes. This representation has a number of advantages: 1) it is a view-independent representation, 2) it is well-suited to rendering with graphics hardware, and 3) it can be computed very quickly. We render these visual hulls with a view-dependent texturing strategy, which takes into account visibility information that is computed during the creation of the visual hull. We demonstrate these algorithms in a system that asynchronously renders dynamically created visual hulls in real-time. Our system outperforms similar systems of comparable computational power. 1

Scene reconstruction, the task of generating a 3D model of a scene given multiple 2D photographs taken of the scene, is an old and difficult problem in computer vision. Since its introduction, scene reconstruction has found application in many fields, including robotics, virtual reality, and entertainment. Volumetric models are a natural choice for scene reconstruction. Three broad classes of volumetric reconstruction techniques have been developed based on geometric intersections, color consistency, and pair-wise matching. Some of these techniques have spawned a number of variations and undergone considerable refinement. This paper is a survey of techniques for volumetric scene reconstruction.

We present a system for generating real-time 3D reconstructions of the user and other real objects in an immersive virtual environment (IVE) for visualization and interaction. For example, when parts of the user's body are in his field of view, our system allows him to see a visually faithful graphical representation of himself, an avatar. In addition, the user can grab real objects, and then see and interact with those objects in the IVE. Our system bypasses an explicit 3D modeling stage, and does not use additional tracking sensors or prior object knowledge, nor do we generate dense 3D representations of objects using computer vision techniques. We use a set of outside-looking-in cameras and a novel visual hull technique that leverages the tremendous recent advances in graphics hardware performance and capabilities. We accelerate the visual hull computation by using projected textures to rapidly determine which volume samples lie within the visual hull. The samples are combined to form the object reconstruction from any given viewpoint. Our system produces results at interactive rates, and because it harnesses ever-improving graphics hardware, the rates and quality should continue to improve. We further examine realtime generated models as active participants in simulations (with lighting) in IVEs, and give results using synthetic and real data.

This paper presents a new class of interactive image editing operations designed to maintain consistency between multiple images of a physical 3D scene. The distinguishing feature of these operations is that edits to any one image propagate automatically to all other images as if the (unknown) 3D scene had itself been modified. The modified scene can then be viewed interactively from any other camera viewpoint and under different scene illuminations. The approach is useful first as a power-assist that enables a user to quickly modify many images by editing just a few, and second as a means for constructing and editing image-based scene representations by manipulating a set of photographs. The approach works by extending operations like image painting, scissoring, and morphing so that they alter a scene’s generalized plenoptic function in a physically-consistent way, thereby affecting scene appearance from all viewpoints simultaneously. A key element in realizing these operations is a new volumetric decomposition technique for reconstructing an scene’s plenoptic function from an incomplete set of camera viewpoints. 1

We present a novel algorithm for simultaneous visual hull reconstruction and rendering by exploiting off-the-shelf graphics hardware. The reconstruction is accomplished by projective texture mapping in conjunction with the alpha test. Parallel to the reconstruction, rendering is also carried out in the graphics pipeline. We texture the visual hull view-dependently with the aid of fragment shaders, such as nVIDIA's register combiners. Both reconstruction and rendering are done in a single rendering pass. We achieve frame rates of more than 80 fps on a standard PC equipped with a commodity graphics card. The performance is significantly faster than that of previously reported similar systems.

Overview We present an image-based approach for capturing the appearance of a walking or running person so they can be rendered realistically under variable viewpoint and illumination. Considerable work has addressed aspects of postproduction control of viewpoint and illumination of a human performance. Most proposed systems address only one of those two aspects e.g. [Wilburn et al. 2005], [Wenger et al. 2005]. [Theobalt et al. 2005] addressed control both of the viewpoint and illumination, however the approach is challenge by low sampling of both lighting and view dimensions. We take a step toward an image-based approach to obtaining postproduction control over both viewpoint and illumination of cyclic full-body human motion by combining the performance relighting technique of [Wenger et al. 2005] with a novel view generation technique based on a flowed reflectance field. By restricting our consideration to cyclic motion such as walking and running, we are able to acquire a 2D array of views by slowly rotating the subject in front of a 1D vertical array of three high speed cameras and segmenting the data per motion cycle. We then use a combination of light field rendering and view interpolation based on optical flow to render the subject from new viewpoints.