Stereo-RSSF: stereo robust sparse scene-flow estimation.

Scene-flow (SF) estimation is considered to be one of the most fundamental problems in scene understanding and autonomous control. The majority of the existing methods adopted for SF estimation suffer lack of robustness in some environments and cannot be easily applied for high-speed applications such as autonomous driving. Although some of the available methods are precise, they include high computational costs or require a GPU. The most serious challenge faced in SF estimation is its inability to strike a balance between speed, precision, robustness, and the computational costs. This paper, therefore, aims at proposing a novel sparse scene-flow (stereo-RSSF) method which is highly distinguished in terms of its faster speed, robustness, and precision using the following: stereo calibrated frames, sparse optical flow such as the LKT algorithm, a new inlier detection module based on spatial correlation analysis, epipolar geometry, and modified circular matching techniques. The comparisons made between stereo-RSSF and several advanced methods indicate that this sparse method has significantly higher accuracy than all the other state-of-the-art methods in the points it estimates. In this paper, the effects of each module and hyper-parameters of stereo-RSSF on the performance and running time are analyzed. Stereo-RSSF has also been evaluated on the KITTI test dataset, and the results have been independently verified by the reference group. The code for our implementation of stereo-RSSF is available at: https://github.com/salehierfan/Stereo-RSSF. [ABSTRACT FROM AUTHOR]