3D Flow

3D flow estimation aims to capture the three-dimensional motion of objects or fluids within a scene, crucial for applications ranging from robotics and autonomous driving to astrophysics and medical imaging. Current research focuses on developing robust and efficient algorithms, often employing deep learning architectures like graph neural networks and recurrent networks, to address challenges such as noise, sparse data, and computational cost in various data modalities (point clouds, images, event streams). These advancements are improving the accuracy and speed of 3D flow estimation, enabling more sophisticated analysis of dynamic scenes and leading to improvements in areas such as 3D action recognition, scene reconstruction, and fluid dynamics simulation.