Papers › An End-to-End Transformer Model for 3D Object Detection
An End-to-End Transformer Model for 3D Object Detection
Ishan Misra, Rohit Girdhar, Armand Joulin
We propose 3DETR, an end-to-end Transformer based object detection model for 3D point clouds. Compared to existing detection methods that employ a number of 3D-specific inductive biases, 3DETR requires minimal modifications to the vanilla Transformer block. Specifically, we find that a standard Transformer with non-parametric queries and Fourier positional embeddings is competitive with specialized architectures that employ libraries of 3D-specific operators with hand-tuned hyperparameters. Nevertheless, 3DETR is conceptually simple and easy to implement, enabling further improvements by incorporating 3D domain knowledge. Through extensive experiments, we show 3DETR outperforms the well-established and highly optimized VoteNet baselines on the challenging ScanNetV2 dataset by 9.5%. Furthermore, we show 3DETR is applicable to 3D tasks beyond detection, and can serve as a building block for future research.
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Code
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Code Syntology ran Syntology
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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 | SUN-RGBD val | 3DETR-m | mAP@0.25 | 59.1 | #23 of 32 | Archive leaderboard | report |
| 3D Object Detection | SUN-RGBD val | 3DETR-m | mAP@0.5 | 32.7 | #23 of 32 | Archive leaderboard | report |
| 3D Object Detection | ScanNetV2 | 3DETR-m | mAP@0.25 | 65.0 | #24 of 33 | Archive leaderboard | report |
| 3D Object Detection | ScanNetV2 | 3DETR-m | mAP@0.5 | 47.0 | #24 of 33 | 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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