The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
Open accessAbstract
A key technical challenge in performing 6D object pose estimation from RGB-D image is to fully leverage the two complementary data sources. Prior works either extract information from the RGB image and depth separately or use costly post-processing steps, limiting their performances in highly cluttered scenes and real-time applications. In this work, we present DenseFusion, a generic framework for estimating 6D pose of a set of known objects from RGB-D images. DenseFusion is a heterogeneous architecture that processes the two data sources individually and uses a novel dense fusion network to extract pixel-wise dense feature embedding, from which the pose is estimated. Furthermore, we integrate an end-to-end iterative pose refinement procedure that further improves the pose estimation while achieving near real-time inference. Our experiments show that our method outperforms state-of-the-art approaches in two datasets, YCB-Video and LineMOD. We also deploy our proposed method to a real robot to grasp and manipulate objects based on the estimated pose.
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Cites
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VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
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Multi-view 3D Object Detection Network for Autonomous Driving
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Frustum PointNets for 3D Object Detection from RGB-D Data
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HybridPose: 6D Object Pose Estimation Under Hybrid Representations
Song, C., Song, J., & Huang, Q. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00051 · Cited by 313
ImVoteNet: Boosting 3D Object Detection in Point Clouds With Image Votes
Qi, C. R., Chen, X., Litany, O., & Guibas, L. J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00446 · Cited by 294
DexYCB: A Benchmark for Capturing Hand Grasping of Objects
Chao, Y.-W., Yang, W., Xiang, Y., Molchanov, P., Handa, A., Tremblay, J., Narang, Y. S., Van Wyk, K., Iqbal, U., Birchfield, S., et al. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00893 · Cited by 285
ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection
Xu, Z., Zhang, W., Ye, X., Tan, X., Yang, W., Wen, S., Ding, E., Meng, A., & Huang, L. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6945 · Cited by 72
Metadata
- Authors
- Wang, C., Xu, D., Zhu, Y., Martín-Martín, R., Lu, C., Fei-Fei, L., & Savarese, S.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 3338-3347
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963177347
- Cited by
- 1,154
- References
- 43
- FWCI
- 210.85
- Type
- conference paper
- Language
- en
- Open access
- green
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- Wang, C., Xu, D., Zhu, Y., Martín-Martín, R., Lu, C., Fei-Fei, L., & Savarese, S. (2019). DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00346
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