Scene Flow Estimation

Scene flow estimation aims to determine the 3D motion of every point in a scene across consecutive frames, typically from LiDAR point clouds or camera images. Current research emphasizes developing efficient and robust methods, often employing deep learning architectures like transformers and recurrent neural networks, to address challenges such as data sparsity, noise, and computational cost, with a growing focus on self-supervised and semi-supervised learning techniques to reduce reliance on expensive labeled data. Accurate scene flow estimation is crucial for applications like autonomous driving, robotics, and human motion analysis, providing essential information for environment understanding and motion prediction.