Seeing 3D objects in a single 2D image

A general framework simultaneously addressing pose estimation, 2D segmentation, object recognition, and 3D reconstruction from a single image is introduced in this paper. The proposed approach partitions 3D space into voxels and estimates the voxel states that maximize a likelihood integrating two components: the object fidelity, that is, the probability that an object occupies the given voxels, here encoded as a 3D shape prior learned from 3D samples of objects in a class; and the image fidelity, meaning the probability that the given voxels would produce the input image when properly projected to the image plane. We derive a loop-less graphical model for this likelihood and propose a computationally efficient optimization algorithm that is guaranteed to produce the global likelihood maximum. Furthermore, we derive a multi-resolution implementation of this algorithm that permits to trade reconstruction and estimation accuracy for computation. The presentation of the proposed framework is complemented with experiments on real data demonstrating the accuracy of the proposed approach.