Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection
Open accessAbstract
The objective of this paper is to learn context- and depth- aware feature representation to solve the problem of monocular 3D object detection. We make following contributions: (i) rather than appealing to the complicated pseudo-LiDAR based approach, we propose a depth-conditioned dynamic message propagation (DDMP) network to effectively integrate the multi-scale depth information with the image context; (ii) this is achieved by first adaptively sampling context-aware nodes in the image context and then dynamically predicting hybrid depth-dependent filter weights and affinity matrices for propagating information; (Hi) by augmenting a center-aware depth encoding (CDE) task, our method successfully alleviates the inaccurate depth prior; (iv) we thoroughly demonstrate the effectiveness of our proposed approach and show state-of-the-art results among the monocular-based approaches on the KITTI benchmark dataset. Particularly, we rank 1stin the highly competitive KITTI monocular 3D object detection track on the submission day (November 16th, 2020). Code and models are released at https: //github.com/fudan-zvg/DDMP
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Traditional approaches that use only RGB images have perceptual limitations in complex real-world tasks, including occlusions, lighting changes, sparse textures, and insufficient semantic information.
“Traditional unimodal approaches (such as relying solely on RGB images) often encounter perceptual limitations when dealing with complex tasks in real-world environments-such as occlusions, lighting variations, sparse textures, and insufficient semantic information [1,2,3].”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [3]
Camera-based single-modal 3D object detection infers 3D information from 2D images; RGB images provide rich visual data but lack direct depth perception, requiring complex estimation, making detection vulnerable to occlusions, poor angles, and adverse weather.
“Camera-based single-modal 3D object detection [1, 72, 2, 3] infers 3D information from 2D images.While RGB images provide rich visual data, the lack of direct depth perception requires complex estimation techniques, making detection vulnerable to occlusions, poor angles, and adverse weather conditions.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [3]
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“Traditional unimodal approaches (such as relying solely on RGB images) often encounter perceptual limitations when dealing with complex tasks in real-world environments—such as occlusions, lighting variations, sparse textures, and insufficient semantic information[1, 2, 3].”
- Authors
- Wang, L., Du, L., Ye, X., Fu, Y., Guo, G., Xue, X., Feng, J., & Zhang, L.
- Affiliations
- Fudan University (CN)
- Baidu (China) (CN)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 454-463
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3172261075
- Cited by
- 146
- References
- 46
- Citations by year
- 2026: 9 · 2025: 36 · 2024: 32 · 2023: 44 · 2022: 22 · 2021: 3
- FWCI
- 8.24
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- monocular 3D object detection, graph neural networks, affinity matrix, KITTI dataset
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