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2013
Conference Paper
Titel
Reconstruction of specular surfaces via probabilistic voxel carving
Abstract
Reconstruction of specular objects from camera images is a notoriously dificult task. We propose a novel approach to recover specular surfaces based on probabilistic voxel carving model. The energy functional formulation of the problem might eventually enable the fusion of all sorts of available information, such as the a priori shape hypotheses, knowledge of motion parameters or the reected environment etc. The model and the specular energy components are discussed in detail. As the first application, we demonstrate the fusion of multiple regularization-free deectometric datasets recorded from different camera positions.