Papers › Do You Remember . . . the Future? Weak-to-Strong generalization in 3D Object Detection
Do You Remember . . . the Future? Weak-to-Strong generalization in 3D Object Detection
Alexander Gambashidze, Aleksandr Dadukin, Maxim Golyadkin, Maria Razzhivina, Ilya Makarov
This paper demonstrates a novel method for LiDAR-based 3D object detection, addressing ma- jor field challenges: sparsity and occlusion. Our approach leverages temporal point cloud sequences to generate frames that provide comprehensive views of objects from multiple angles. To address the challenge of generating these frames in real- time, we employ Knowledge Distillation within a Teacher-Student framework, allowing the Stu- dent model to emulate the Teacher’s advanced per- ception. We pioneered the application of weak- to-strong generalization in computer vision by training our Teacher model on enriched, object- complete data. In this demo, we showcase the ex- ceptional quality of labels produced by the X-Ray Teacher on object-complete frames, showing our method distilling its knowledge to enhance object 3D detection models.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Object Detection | Waymo Open Dataset | X-Ray DSVT Pillar-Scaled | mAPH/L2 | 71.4 | #5 of 8 | Archive leaderboard | report |
| 3D Object Detection | nuScenes | X-Ray CenterPoint-Voxel | NDS | 0.63 | #151 of 372 | Archive leaderboard | report |
| 3D Object Detection | nuScenes | X-Ray CenterPoint-Voxel | mAP | 0.54 | #151 of 372 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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