3D Object Detection

3D object detection predicts what objects are present and where they are in three-dimensional space, often as labeled bounding boxes. Methods include LiDAR-based point, voxel, and pillar networks; camera systems that lift image features into bird’s-eye view; and fusion of cameras, LiDAR, and radar, sometimes with temporal or cooperative data from multiple vehicles. Researchers evaluate these approaches on driving benchmarks such as KITTI, nuScenes, and Waymo, as well as indoor, medical, aerial, and other specialized datasets. A central challenge is balancing accurate detection of small, distant, or occluded objects with efficient inference, limited labels, and reliability under changes in sensors, weather, and deployment conditions.