This paper aims for high-performance offline LiDAR-based 3D object detection. We first observe that experienced human annotators annotate objects from a track-centric perspective. They first label the objects with clear shapes in a track, and then leverage the temporal coherence to infer the annotations of obscure objects. Drawing inspiration from this, we propose a high-performance offline detector in a track-centric perspective instead of the conventional object-centric perspective. Our method features a bidirectional tracking module and a track-centric learning module. Such a design allows our detector to infer and refine a complete track once the object is detected at a certain moment. We refer to this characteristic as "onCe detecTed, neveR Lost" and name the proposed system CTRL. Extensive experiments demonstrate the remarkable performance of our method, surpassing the human-level annotating accuracy and the previous state-of-the-art methods in the highly competitive Waymo Open Dataset without model ensemble. The code will be made publicly available at https://github.com/tusen-ai/SST.
@article{arxiv.2304.12315,
title = {Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection},
author = {Lue Fan and Yuxue Yang and Yiming Mao and Feng Wang and Yuntao Chen and Naiyan Wang and Zhaoxiang Zhang},
journal= {arXiv preprint arXiv:2304.12315},
year = {2023}
}