Spacetime Stereo and 3D Flow via Binocular Spatiotemporal Orientation Analysis.

This paper presents a novel approach to recovering estimates of 3D structure and motion of a dynamic scene from a sequence of binocular stereo images. The approach is based on matching spatiotemporal orientation distributions between left and right temporal image streams, which encapsulates both local spatial and temporal structure for disparity estimation. By capturing spatial and temporal structure in this unified fashion, both sources of information combine to yield disparity estimates that are naturally temporal coherent, while helping to resolve matches that might be ambiguous when either source is considered alone. Further, by allowing subsets of the orientation measurements to support different disparity estimates, an approach to recovering multilayer disparity from spacetime stereo is realized. Similarly, the matched distributions allow for direct recovery of dense, robust estimates of 3D scene flow. The approach has been implemented with real-time performance on commodity GPUs using OpenCL. Empirical evaluation shows that the proposed approach yields qualitatively and quantitatively superior estimates in comparison to various alternative approaches, including the ability to provide accurate multilayer estimates in the presence of (semi)transparent and specular surfaces.