Perception Datasets

Perception datasets are crucial for training and evaluating computer vision algorithms, particularly in robotics and autonomous systems. Current research emphasizes the creation of large-scale, multi-modal datasets capturing diverse scenarios and environmental conditions, often incorporating data from multiple sensors (e.g., LiDAR, cameras, radar) and addressing challenges like occlusion and low-light conditions. These datasets are driving advancements in cooperative perception, where information from multiple agents is fused, and in self-supervised learning methods that reduce the need for extensive manual annotation. The availability of high-quality, publicly accessible perception datasets is essential for accelerating progress in autonomous driving, robotics, and other fields reliant on robust environmental understanding.