Papers › Ground-aware Monocular 3D Object Detection for Autonomous Driving

Ground-aware Monocular 3D Object Detection for Autonomous Driving

1 Feb 2021arXiv:2102.00690archive 2025-07-28

Yuxuan Liu, Yuan Yixuan, Ming Liu

Estimating the 3D position and orientation of objects in the environment with a single RGB camera is a critical and challenging task for low-cost urban autonomous driving and mobile robots. Most of the existing algorithms are based on the geometric constraints in 2D-3D correspondence, which stems from generic 6D object pose estimation. We first identify how the ground plane provides additional clues in depth reasoning in 3D detection in driving scenes. Based on this observation, we then improve the processing of 3D anchors and introduce a novel neural network module to fully utilize such application-specific priors in the framework of deep learning. Finally, we introduce an efficient neural network embedded with the proposed module for 3D object detection. We further verify the power of the proposed module with a neural network designed for monocular depth prediction. The two proposed networks achieve state-of-the-art performances on the KITTI 3D object detection and depth prediction benchmarks, respectively. The code will be published in https://www.github.com/Owen-Liuyuxuan/visualDet3D

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Owen-Liuyuxuan/visualDet3D officialmentioned in papermentioned on GitHubpytorch report

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Tasks

3D Object Detection 6D Pose Estimation using RGB Autonomous Driving Depth Estimation Depth Prediction Efficient Neural Network Monocular 3D Object Detection Object Object Detection Pose Estimation object-detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Monocular 3D Object Detection	KITTI Cars Hard	GAC	AP Hard	9.94	#6 of 7	Archive leaderboard	report
Monocular 3D Object Detection	KITTI Cars Moderate	GAC	AP Medium	13.17	#13 of 29	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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