Motion estimation and prediction from 3D point clouds - Li, Ruibo (Nanyang Technological University, 2024)
Understanding the motion of dynamic environments holds significant benefits for various applications, including robotics and autonomous driving. Scene flow estimation from 3D point clouds, which outputs a per-point 3D motion field between two consecutive time steps, has garnered increasing attention. Although deep learning-based scene flow models have shown promising results, how to capture motion from sparse and irregular point cloud data remains an open question. Furthermore, supervised training of these models demands substantial training data with scene flow annotations, which is both scarce and expensive to collect. To reduce the reliance on scene flow annotations, self-supervised scene flow estimation has emerged as a viable solution, where no annotations are required during training.