SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
This provides a novel approach to scene flow estimation for automotive applications, though it builds incrementally on existing interpolation and refinement techniques.
The paper tackles scene flow estimation by introducing a method that densely interpolates sparse matches across stereo image pairs, achieving state-of-the-art results on the KITTI benchmark with an average error of 4.27% and competitive performance on MPI Sintel.
While most scene flow methods use either variational optimization or a strong rigid motion assumption, we show for the first time that scene flow can also be estimated by dense interpolation of sparse matches. To this end, we find sparse matches across two stereo image pairs that are detected without any prior regularization and perform dense interpolation preserving geometric and motion boundaries by using edge information. A few iterations of variational energy minimization are performed to refine our results, which are thoroughly evaluated on the KITTI benchmark and additionally compared to state-of-the-art on MPI Sintel. For application in an automotive context, we further show that an optional ego-motion model helps to boost performance and blends smoothly into our approach to produce a segmentation of the scene into static and dynamic parts.