The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
BEVDepth: Acquisition of Reliable Depth for Multi-View 3D Object Detection
Open accessAbstract
In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird's-Eye-View~(BEV) 3D object detection. Our work is based on a key observation -- depth estimation in recent approaches is surprisingly inadequate given the fact that depth is essential to camera 3D detection. Our BEVDepth resolves this by leveraging explicit depth supervision. A camera-awareness depth estimation module is also introduced to facilitate the depth predicting capability. Besides, we design a novel Depth Refinement Module to counter the side effects carried by imprecise feature unprojection. Aided by customized Efficient Voxel Pooling and multi-frame mechanism, BEVDepth achieves the new state-of-the-art 60.9% NDS on the challenging nuScenes test set while maintaining high efficiency. For the first time, the NDS score of a camera model reaches 60%. Codes have been released.
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Self-citationSqueeze-and-Excitation Networks
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End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · arXiv · doi:10.48550/arxiv.2005.12872 · Cited by 18,253
Are we ready for autonomous driving? The KITTI vision benchmark suite
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Decoupled Weight Decay Regularization
Loshchilov, I., & Hutter, F. (2017) · arXiv · doi:10.48550/arxiv.1711.05101 · Cited by 12,522
Squeeze-and-Excitation Networks
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913372 · Cited by 12,157
nuScenes: A Multimodal Dataset for Autonomous Driving
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VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
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Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection
Wang, S., Liu, Y., Wang, T., Li, Y., & Zhang, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00335 · Cited by 292
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Wang, X., Zhu, Z., Xu, W., Zhang, Y., Wei, Y., Chi, X., Ye, Y., Du, D., Lu, J., & Wang, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01636 · Cited by 192
Cross Modal Transformer: Towards Fast and Robust 3D Object Detection
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Self-citationCRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception
Kim, Y., Shin, J., Kim, S., Lee, I.-J., Choi, J. W., & Kum, D. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01615 · Cited by 155
Metadata
- Authors
- Li, Y., Ge, Z., Yu, G., Yang, J., Wang, Z., Shi, Y., Sun, J., & Li, Z.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2023-06-26
- Volume
- 37
- Issue
- 2
- Pages
- 1477-1485
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W4382464460
- Cited by
- 769
- References
- 51
- FWCI
- 87.33
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- CoChat. (2026). Citation check: BEVDepth: Acquisition of Reliable Depth for Multi-View 3D Object Detection. https://app.cochat.ai/papers/10.1609/aaai.v37i2.25233
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- Li, Y., Ge, Z., Yu, G., Yang, J., Wang, Z., Shi, Y., Sun, J., & Li, Z. (2023). BEVDepth: Acquisition of Reliable Depth for Multi-View 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v37i2.25233
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