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Pattern Recognition
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This paper introduces the novel volumetric methodology "appearance-cloning" as a viable solution for achieving a more improved photo-consistent scene recovery, including a greatly enhanced geometric recovery performance, from a set of photographs taken at arbitrarily distributed multiple camera viewpoints. We do so while solving many of the problems associated with previous stereo-based and volumetric methodologies. We redesign the photo-consistency decision problem of individual voxel in volumetric space as the photo-consistent shape search problem in image space, by generalizing the concept of the point correspondence search between two images in stereo-based approach, within a volumetric framework.In detail, we introduce a self-constrained greedy-style optimization methodology, which iteratively searches a more photo-consistent shape based on the probabilistic shape photo-consistency measure, by using the probabilistic competition between candidate shapes. Our new measure is designed to bring back the probabilistic photo-consistency of a shape by comparing the appearances captured from multiple cameras with those rendered from that shape using the per-pixel Maxwell model in image space.Through various scene recoveries experiments including specular and dynamic scenes, we demonstrate that if sufficient appearances are given enough to reflect scene characteristics, our appearance-cloning approach can successfully recover both the geometry and photometry information of a scene without any kind of scene-dependent algorithm tuning.