RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization Article Swipe
Yan Xu
,
Kwan-Yee Lin
,
Guofeng Zhang
,
Xiaogang Wang
,
Hongsheng Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.12870
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.12870
6-DoF object pose estimation from a monocular image is challenging, and a post-refinement procedure is generally needed for high-precision estimation. In this paper, we propose a framework based on a recurrent neural network (RNN) for object pose refinement, which is robust to erroneous initial poses and occlusions. During the recurrent iterations, object pose refinement is formulated as a non-linear least squares problem based on the estimated correspondence field (between a rendered image and the observed image). The problem is then solved by a differentiable Levenberg-Marquardt (LM) algorithm enabling end-to-end training. The correspondence field estimation and pose refinement are conducted alternatively in each iteration to recover the object poses. Furthermore, to improve the robustness to occlusion, we introduce a consistency-check mechanism based on the learned descriptors of the 3D model and observed 2D images, which downweights the unreliable correspondences during pose optimization. Extensive experiments on LINEMOD, Occlusion-LINEMOD, and YCB-Video datasets validate the effectiveness of our method and demonstrate state-of-the-art performance.
Related Topics
Concepts
Pose
3D pose estimation
Artificial intelligence
Robustness (evolution)
Articulated body pose estimation
Computer science
Computer vision
Monocular
Differentiable function
Consistency (knowledge bases)
Object (grammar)
Pattern recognition (psychology)
Mathematics
Chemistry
Biochemistry
Mathematical analysis
Gene
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2203.12870
- https://arxiv.org/pdf/2203.12870
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221161044
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4221161044Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2203.12870Digital Object Identifier
- Title
-
RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose OptimizationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-03-24Full publication date if available
- Authors
-
Yan Xu, Kwan-Yee Lin, Guofeng Zhang, Xiaogang Wang, Hongsheng LiList of authors in order
- Landing page
-
https://arxiv.org/abs/2203.12870Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2203.12870Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2203.12870Direct OA link when available
- Concepts
-
Pose, 3D pose estimation, Artificial intelligence, Robustness (evolution), Articulated body pose estimation, Computer science, Computer vision, Monocular, Differentiable function, Consistency (knowledge bases), Object (grammar), Pattern recognition (psychology), Mathematics, Chemistry, Biochemistry, Mathematical analysis, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.monocular | 6 |
| abstract_inverted_index.procedure | 13 |
| abstract_inverted_index.recurrent | 30, 49 |
| abstract_inverted_index.training. | 89 |
| abstract_inverted_index.end-to-end | 88 |
| abstract_inverted_index.estimation | 3, 93 |
| abstract_inverted_index.formulated | 55 |
| abstract_inverted_index.non-linear | 58 |
| abstract_inverted_index.occlusion, | 114 |
| abstract_inverted_index.refinement | 53, 96 |
| abstract_inverted_index.robustness | 112 |
| abstract_inverted_index.unreliable | 136 |
| abstract_inverted_index.demonstrate | 156 |
| abstract_inverted_index.descriptors | 124 |
| abstract_inverted_index.downweights | 134 |
| abstract_inverted_index.estimation. | 19 |
| abstract_inverted_index.experiments | 142 |
| abstract_inverted_index.iterations, | 50 |
| abstract_inverted_index.occlusions. | 46 |
| abstract_inverted_index.refinement, | 37 |
| abstract_inverted_index.Furthermore, | 108 |
| abstract_inverted_index.challenging, | 9 |
| abstract_inverted_index.performance. | 158 |
| abstract_inverted_index.alternatively | 99 |
| abstract_inverted_index.effectiveness | 151 |
| abstract_inverted_index.optimization. | 140 |
| abstract_inverted_index.correspondence | 66, 91 |
| abstract_inverted_index.differentiable | 83 |
| abstract_inverted_index.high-precision | 18 |
| abstract_inverted_index.correspondences | 137 |
| abstract_inverted_index.post-refinement | 12 |
| abstract_inverted_index.state-of-the-art | 157 |
| abstract_inverted_index.consistency-check | 118 |
| abstract_inverted_index.Occlusion-LINEMOD, | 145 |
| abstract_inverted_index.Levenberg-Marquardt | 84 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |