Center-Based 3D Object Detection and Tracking

Tianwei Yin, Xingyi Zhou, Philipp Krahenbuhl; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 11784-11793

Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This representation mimics the well-studied image-based 2D bounding-box detection but comes with additional challenges. Objects in a 3D world do not follow any particular orientation, and box-based detectors have difficulties enumerating all orientations or fitting an axis-aligned bounding box to rotated objects. In this paper, we instead propose to represent, detect, and track 3D objects as points. Our framework, CenterPoint, first detects centers of objects using a keypoint detector and regresses to other attributes, including 3D size, 3D orientation, and velocity. In a second stage, it refines these estimates using additional point features on the object. In CenterPoint, 3D object tracking simplifies to greedy closest-point matching. The resulting detection and tracking algorithm is simple, efficient, and effective. On the nuScenes and Waymo datasets, CenterPoint surpasses prior methods by a large margin. On the Waymo Open Dataset, CenterPoint improves previous state-of-the-art by 10-20% while running at 13FPS. The code and pretrained models are available at https://github.com/tianweiy/CenterPoint.