The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation
Open accessAbstract
Estimating 3D orientation and translation of objects is essential for infrastructure-less autonomous navigation and driving. In case of monocular vision, successful methods have been mainly based on two ingredients: (i) a network generating 2D region proposals, (ii) a R-CNN structure predicting 3D object pose by utilizing the acquired regions of interest. We argue that the 2D detection network is redundant and introduces non-negligible noise for 3D detection. Hence, we propose a novel 3D object detection method, named SMOKE, in this paper that predicts a 3D bounding box for each detected object by combining a single keypoint estimate with regressed 3D variables. As a second contribution, we propose a multi-step disentangling approach for constructing the 3D bounding box, which significantly improves both training convergence and detection accuracy. In contrast to previous 3D detection techniques, our method does not require complicated pre/post-processing, extra data, and a refinement stage. Despite of its structural simplicity, our proposed SMOKE network outperforms all existing monocular 3D detection methods on the KITTI dataset, giving the best state-of-the-art result on both 3D object detection and Bird’s eye view evaluation. The code is available at https://github.com/lzccccc/SMOKE.
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Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
(2016) · Cited by 31,590
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
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PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
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Cited by
Categorical Depth Distribution Network for Monocular 3D Object Detection
Reading, C., Harakeh, A., Chae, J., & Waslander, S. L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00845 · Cited by 595
Is Pseudo-Lidar needed for Monocular 3D Object detection?
Park, D., Ambrus, R., Guizilini, V., Li, J., & Gaidon, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00313 · Cited by 334
MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships
Chen, Y., Tai, L., Sun, K., & Li, M. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01211 · Cited by 316
Delving into Localization Errors for Monocular 3D Object Detection
Ma, X., Zhang, Y., Xu, D., Zhou, D., Yi, S., Li, H., & Ouyang, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00469 · Cited by 268
Rukhovich, D., Vorontsova, A., & Konushin, A. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00133 · Cited by 251
Metadata
- Authors
- Liu, Z., Wu, Z., & Toth, R.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 4289-4298
- OpenAlex
- W3035254347
- Cited by
- 455
- References
- 46
- FWCI
- 17.19
- Type
- conference paper
- Language
- en
- Open access
- green
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- Liu, Z., Wu, Z., & Toth, R. (2020). SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/cvprw50498.2020.00506
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