Shape from Silhouette Consensus

This paper proposes a shape-from-silhouette algorithm that is robust to inconsistent silhouettes, often common in real applications due to occlusions, errors in the background subtraction, noise or even calibration errors. The recovery of the shape that best fits the available data (silhouettes) is formulated as a continuous energy minimization problem. The energy is based on the error between the silhouettes and the shape plus a regularization term. Thanks to the characterization of the visible surface in each view as a function of the shape, we consider the error in the volume space. As a result, we obtain an iterative volume-based algorithm that evolves the initial shape to the shape that is in general agreement with the silhouettes, thus being robust to errors in the silhouettes. We have implemented the proposed algorithm in a graphics card processor with parallel computing allowing reduced computational times. The obtained results compare favorably to those of the state of the art.