Hierarchical belief propagation to reduce search space using CUDA for stereo and motion estimation

This paper describes a hierarchical belief propagation implementation in which a `rough' disparity map calculation or motion estimation in higher levels is used to limit the search space and enable the calculation of the desired disparity map/set of motion vectors using a smaller search space than traditional belief propagation. We implement our algorithm on the GPU using the CUDA architecture and explore a number of implementation details with promising results; it is clear that the storage requirements of belief propagation can be significantly reduced using our method without too large of a sacrifice in the accuracy of the results. In addition, we take advantage of the interpolation capabilities built into the GPU in order to retrieve the computed disparities/motion vectors at sub-pixel accuracy without making any change in implementation.