Scene Flow

Scene flow aims to estimate the 3D motion of points in a scene across consecutive frames, providing a rich representation of dynamic environments. Current research focuses on improving accuracy and efficiency, particularly for large-scale point clouds from LiDAR and other sensors, employing various neural network architectures including transformers and diffusion models, as well as optimization-based methods. These advancements are crucial for applications like autonomous driving, robotics, and medical imaging, enabling more robust perception and understanding of dynamic scenes. The field is also actively addressing challenges such as handling occlusions, small objects, and noisy data, often through self-supervised or semi-supervised learning techniques.