Dense Optical Flow

Dense optical flow estimation aims to compute the motion of every pixel in a video sequence, providing a rich representation of scene dynamics. Current research focuses on improving accuracy and robustness in challenging conditions (e.g., low light, fast motion) using techniques like self-supervised learning, neural implicit representations, and hybrid approaches combining deep learning with traditional methods. These advancements are driving progress in applications such as autonomous driving, 3D reconstruction, video synthesis, and robotic manipulation, where accurate motion understanding is crucial.