Center-based 3D Object Detection and Tracking

Code

• github.com/tianweiy/CenterPoint
  OfficialIn paperpytorch★ 2,130
• github.com/open-mmlab/mmdetection3d
  pytorch★ 6,327
• github.com/nvidia-ai-iot/lidar_ai_solution
  pytorch★ 1,767
• github.com/PaddlePaddle/Paddle3D
  paddle★ 638
• github.com/livox-sdk/livox_detection
  pytorch★ 314
• github.com/CarkusL/CenterPoint
  pytorch★ 225
• github.com/tianweiy/CenterPoint-KITTI
  pytorch★ 210
• github.com/chowkamlee81/CentrePointNet
  pytorch★ 11
• github.com/zion-king/Center-based-3D-Object-Detection-and-Tracking
  pytorch★ 11
• github.com/mon95/centerpoint-maps
  pytorch★ 7

Abstract

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. CenterPoint achieved state-of-the-art performance on the nuScenes benchmark for both 3D detection and tracking, with 65.5 NDS and 63.8 AMOTA for a single model. On the Waymo Open Dataset, CenterPoint outperforms all previous single model method by a large margin and ranks first among all Lidar-only submissions. The code and pretrained models are available at https://github.com/tianweiy/CenterPoint.

Tasks

Benchmark Results

Reproductions