# Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection Source: [Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:15:37.285Z Authors: Lue Fan, Yuxue Yang, Yiming Mao, Feng Wang, Yuntao Chen, Naiyan Wang, Zhaoxiang Zhang Published: 2023-04-24T17:59:05.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D Multi-Object Tracking on Waymo (test)](): 3D Multi-Object Tracking; Waymo (test); MOTA: 73.29. ## Original sources and implementation [Original paper]() [PDF]()