In order to address the issues of scene over-smoothing and motion edge-blurring caused by the existing RGBD scene flow methods under complex scenes

non-rigid movement and motion occlusions

this paper proposes a RGBD scene flow method based on FRFCM (Fast and Robust Fuzzy C-Means) clustering and depth optimization. First

the optical flow information from the consecutive frames is marked as the benchmark and the FRFCM clustering approach is utilized to obtain the initial segmentation of the input image sequences. Second

according to the motion edge information of the depth image

we further optimize the initial segmentation to extract the high-confidence hierarchical motion information. Finally

an energy function of RGBD scene flow based on image segmentation is designed

and the pyramid warping strategy is adopted to compute the scene flow field. We employ the test sets of Middlebury and MPI-Sintel databases to conduct a comparison experiment between the proposed method and the existing RGBD scene flow methods. The experimental results indicate that the proposed method has better accuracy and robustness of scene flow estimation

especially when dealing with the issues of scene over-smoothing and motion edge-blurring.