Papers › Is Pseudo-Lidar needed for Monocular 3D Object detection?
Is Pseudo-Lidar needed for Monocular 3D Object detection?
Dennis Park, Rares Ambrus, Vitor Guizilini, Jie Li, Adrien Gaidon
Recent progress in 3D object detection from single images leverages monocular depth estimation as a way to produce 3D pointclouds, turning cameras into pseudo-lidar sensors. These two-stage detectors improve with the accuracy of the intermediate depth estimation network, which can itself be improved without manual labels via large-scale self-supervised learning. However, they tend to suffer from overfitting more than end-to-end methods, are more complex, and the gap with similar lidar-based detectors remains significant. In this work, we propose an end-to-end, single stage, monocular 3D object detector, DD3D, that can benefit from depth pre-training like pseudo-lidar methods, but without their limitations. Our architecture is designed for effective information transfer between depth estimation and 3D detection, allowing us to scale with the amount of unlabeled pre-training data. Our method achieves state-of-the-art results on two challenging benchmarks, with 16.34% and 9.28% AP for Cars and Pedestrians (respectively) on the KITTI-3D benchmark, and 41.5% mAP on NuScenes.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Monocular 3D Object Detection | KITTI Cars Easy | DD3D | AP Easy | 23.22 | #4 of 5 | Archive leaderboard | report |
| Monocular 3D Object Detection | KITTI Cars Hard | DD3D | AP Hard | 14.20 | #4 of 7 | Archive leaderboard | report |
| Monocular 3D Object Detection | KITTI Cars Moderate | DD3D | AP Medium | 16.34 | #7 of 29 | Archive leaderboard | report |
| Monocular 3D Object Detection | KITTI Pedestrian Easy | DD3D | AP Easy | 13.91 | #2 of 3 | Archive leaderboard | report |
| Monocular 3D Object Detection | KITTI Pedestrian Hard | DD3D | AP Hard | 8.05 | #1 of 4 | Archive leaderboard | report |
| Monocular 3D Object Detection | KITTI Pedestrian Moderate | DD3D | AP Medium | 9.30 | #1 of 3 | 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.
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