Scene Flow Benchmark

Scene flow estimation, the task of computing the 3D motion of pixels in a scene across consecutive frames, is crucial for applications like autonomous driving and robotics. Recent research focuses on improving accuracy and robustness, particularly through the use of diffusion models and transformer-based architectures, often incorporating self-supervised or semi-supervised learning techniques to address the scarcity of labeled data. High-resolution datasets with detailed ground truth are being developed to better evaluate and push the limits of these methods, leading to advancements in both model performance and uncertainty quantification. These improvements are driving progress in various computer vision tasks and enabling more reliable perception in real-world applications.