Poxels: Probabilistic Voxelized Volume Reconstruction

Abstract

This paper examines the problem of reconstructing a voxelized representation of 3D space from a series of im-ages. An iterative algorithm is used to find the scene model which jointly explains all the observed images by determin-ing which region of space is responsible for each of the ob-servations. The current approach formulates the problem as one of optimization over estimates of these responsibil-ities. The process converges to a distribution of responsi-bility which accurately reflects the constraints provided by the observations, the positions and shape of both solid and transparent objects, and the uncertainty which remains. Reconstruction is robust, and gracefully represents regions of space in which there is little certainty about the ex-act structure due to limited, non-existent, or contradicting data. Rendered images of voxel spaces recovered from syn-thetic and real observation images are shown. 1

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