FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection
Open accessAbstract
Monocular 3D object detection is an important task for autonomous driving considering its advantage of low cost. It is much more challenging than conventional 2D cases due to its inherent ill-posed property, which is mainly reflected in the lack of depth information. Recent progress on 2D detection offers opportunities to better solving this problem. However, it is non-trivial to make a general adapted 2D detector work in this 3D task. In this paper, we study this problem with a practice built on a fully convolutional single-stage detector and propose a general framework FCOS3D. Specifically, we first transform the commonly defined 7-DoF 3D targets to the image domain and decouple them as 2D and 3D attributes. Then the objects are distributed to different feature levels with consideration of their 2D scales and assigned only according to the projected 3D-center for the training procedure. Furthermore, the center-ness is redefined with a 2D Gaussian distribution based on the 3D-center to fit the 3D target formulation. All of these make this framework simple yet effective, getting rid of any 2D detection or 2D-3D correspondence priors. Our solution achieves 1st place out of all the vision-only methods in the nuScenes 3D detection challenge of NeurIPS 2020. Code and models are released at https://github.com/open-mmlab/mmdetection3d.
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BEVFormer's inference speed is comparable with other methods [45,47].
“Although we utilize temporal information, our inference speed is still comparable with other methods [45, 47].”
From BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers (2022) · cites this paper as [45]
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Is Pseudo-Lidar needed for Monocular 3D Object detection?
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“For details, see [2].”
MethodResultLi, Z., Wang, W., Li, H., Xie, E., Sima, C., Lu, T., Yu, Q., & Dai, J. (2022) · arXiv · doi:10.48550/arxiv.2203.17270 · Cited by 1,419 · see citing works
“Method Modality Backbone NDS↑ mAP↑ mATE↓ mASE↓ mAOE↓ mAVE↓ mAAE↓ FCOS3D [45] C R101 0.”
BackgroundMethodInfluentialRcm-fusion: Radar-camera multi-level fusion for 3d object detection
Kim, J., Seong, M., Bang, G., Kum, D., & Choi, J. W. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10611449 · Cited by 33 · see citing works
Multi-Sem Fusion: Multimodal Semantic Fusion for 3-D Object Detection
Xu, S., Li, F., Song, Z., Fang, J., Wang, S., & Yang, Z.-X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3387732 · Cited by 26 · see citing works
- Authors
- Wang, T., Zhu, X., Pang, J., & Lin, D.
- Affiliations
- Chinese University of Hong Kong (HK)
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 913-922
- ISSN
- 2473-9936 (ISSN-L)
- OpenAlex
- W3215100485
- Cited by
- 683
- References
- 12
- Citations by year
- 2026: 88 · 2025: 169 · 2024: 216 · 2023: 157 · 2022: 49 · 2021: 4
- FWCI
- 34.83
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Computer Vision Workshops (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- monocular 3D object detection, one-stage detectors, autonomous driving, 2D-3D correspondence
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