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 arbitrarily-distributed 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) capture 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 view-dependent effects on scene-appearance.

A Theory of Shape by Space Carving

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. The approach is evaluated with images from both inward-facing and outward-facing cameras.

Photorealistic Scene Reconstruction by Voxel Coloring

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. The approach is evaluated with images from both inward- and outward-facing cameras.

/pdf/photorealistic-scene-reconstruction-by-voxel-coloring-1zysfw9zjh.pdf

Photorealistic scene reconstruction by voxel coloring

Three-dimensional (3D) image acquisition systems are rapidly becoming more affordable, especially systems based on commodity electronic cameras. At the same time, personal computers with graphics hardware capable of displaying complex 3D models are also becoming inexpensive enough to be available to a large population. As a result, there is potentially an opportunity to consider new virtual reality applications as diverse as cultural heritage and retail sales that will allow people to view realistic 3D objects on home computers. Although there are many physical techniques for acquiring 3D data—including laser scanners, structured light and time-of-flight—there is a basic pipeline of operations for taking the acquired data and producing a usable numerical model. We look at the fundamental problems of range image registration, line-of-sight errors, mesh integration, surface detail and color, and texture mapping. In the area of registration we consider both the problems of finding an initial global alignment using manual and automatic means, and refining this alignment with variations of the Iterative Closest Point methods. To account for scanner line-of-sight errors we compare several averaging approaches. In the area of mesh integration, that is finding a single mesh joining the data from all scans, we compare various methods for computing interpolating and approximating surfaces. We then look at various ways in which surface properties such as color (more properly, spectral reflectance) can be extracted from acquired imagery. Finally, we examine techniques for producing a final model representation that can be efficiently rendered using graphics hardware.

/pdf/the-3d-model-acquisition-pipeline-4c4s96rxbu.pdf

The 3D Model Acquisition Pipeline

The 3D Model Acquisition Pipeline.

: Traditionally, the matching problem in stereo vision has been formulated as an ill-posed problem. However, it has been shown that the matching problem can be well-posed in scenes with no occlusions or depth discontinuities. Unfortunately most real scenes do not obey these constraints. We overcome this by finding regions within the images in which the matching problem is well-posed. That is, we find image regions in which there are no occlusions or depth discontinuities. In general, a unique set of such regions does not exist. However, we will demonstrate that in almost all cases these regions can be found efficiently. Therefore the matching problem can be well-posed in almost all cases. In order to find these corresponding regions we transform the problem from finding 2D image regions into identifying 3D surfaces. We have developed a method of 3D surface extraction which uniquely identifies correct 3D surfaces from a set of potential surfaces. In order to test the method we have built a four camera system with which we will present results from several scenes.

/pdf/multi-baseline-stereo-using-surface-extraction-o28042vao5.pdf

Multi-Baseline Stereo Using Surface Extraction.

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Multi-Baseline Stereo Using Surface Extraction.