LiDAR Scene Flow

LiDAR scene flow aims to estimate the three-dimensional motion of points in a scene captured by LiDAR sensors, crucial for autonomous driving and robotics. Research focuses on improving accuracy and efficiency through various approaches, including deep learning methods utilizing 4D voxel networks and spatio-temporal convolutions, as well as more computationally efficient techniques like kernel-based methods and iterative closest point (ICP) algorithms. These advancements are driven by the need for real-time performance and robustness in challenging real-world scenarios, with applications ranging from self-supervised 3D object detection to improved scene understanding for autonomous navigation. Recent work highlights the importance of addressing limitations in existing benchmarks and the significant contribution of pre- and post-processing steps to overall performance.