Center-based 3D Object Detection and Tracking

TL;DR

CenterPoint introduces a point-based framework for 3D object detection and tracking in point-clouds, simplifying the process and achieving state-of-the-art results on major benchmarks.

Contribution

The paper proposes a novel point-based detection and tracking method, CenterPoint, which improves efficiency and accuracy over traditional box-based approaches.

Findings

Achieved 65.5 NDS on nuScenes benchmark.

Outperformed previous methods on Waymo Open Dataset.

Simplified 3D tracking with greedy closest-point matching.

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.