Papers › RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving
RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving
Peixuan Li, Huaici Zhao, PengFei Liu, Feidao Cao
In this work, we propose an efficient and accurate monocular 3D detection framework in single shot. Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Four edges of a 2D box provide only four constraints and the performance deteriorates dramatically with the small error of the 2D detector. Different from these approaches, our method predicts the nine perspective keypoints of a 3D bounding box in image space, and then utilize the geometric relationship of 3D and 2D perspectives to recover the dimension, location, and orientation in 3D space. In this method, the properties of the object can be predicted stably even when the estimation of keypoints is very noisy, which enables us to obtain fast detection speed with a small architecture. Training our method only uses the 3D properties of the object without the need for external networks or supervision data. Our method is the first real-time system for monocular image 3D detection while achieves state-of-the-art performance on the KITTI benchmark. Code will be released at https://github.com/Banconxuan/RTM3D.
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Code
Syntology Ran 11 of 16 code samples harvested from 2 repositories linked to this paper; 5 have no recorded run. Of those that ran: 3 ran · honoured contract; 3 ran · our draft was wrong; 5 ran with no contract checked.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Vehicle Pose Estimation | KITTI Cars Hard | RTM-3D | Average Orientation Similarity | 77.18 | #6 of 19 | 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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