Dense Scene Flow Based on Depth and Multi-channel Bilateral Filter

There is close relationship between depth information and scene flow. However, it’s not fully utilized in most of scene flow estimators. In this paper, we propose a method to estimate scene flow with monocular appearance images and corresponding depth images. We combine a global energy optimization and a bilateral filter into a two-step framework. Occluded pixels are detected by the consistency of appearance and depth, and the corresponding data errors are excluded from the energy function. The appearance and depth information are also utilized in anisotropic regularization to suppress over-smoothing. The multi-channel bilateral filter is introduced to correct scene flow with various information in non-local areas. The proposed approach is tested on Middlebury dataset and the sequences captured by KINECT. Experiment results show that it can estimate dense and accurate scene flow in challenging environments and keep the discontinuity around motion boundaries.