StablePose: Learning 6D Object Poses from Geometrically Stable Patches Article Swipe
Yifei Shi
,
Junwen Huang
,
Xin Xu
,
Yifan Zhang
,
Kai Xu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.09334
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.09334
We introduce the concept of geometric stability to the problem of 6D object pose estimation and propose to learn pose inference based on geometrically stable patches extracted from observed 3D point clouds. According to the theory of geometric stability analysis, a minimal set of three planar/cylindrical patches are geometrically stable and determine the full 6DoFs of the object pose. We train a deep neural network to regress 6D object pose based on geometrically stable patch groups via learning both intra-patch geometric features and inter-patch contextual features. A subnetwork is jointly trained to predict per-patch poses. This auxiliary task is a relaxation of the group pose prediction: A single patch cannot determine the full 6DoFs but is able to improve pose accuracy in its corresponding DoFs. Working with patch groups makes our method generalize well for random occlusion and unseen instances. The method is easily amenable to resolve symmetry ambiguities. Our method achieves the state-of-the-art results on public benchmarks compared not only to depth-only but also to RGBD methods. It also performs well in category-level pose estimation.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2102.09334
- https://arxiv.org/pdf/2102.09334
- OA Status
- green
- Cited By
- 2
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3131493032
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- OpenAlex ID
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https://openalex.org/W3131493032Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2102.09334Digital Object Identifier
- Title
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StablePose: Learning 6D Object Poses from Geometrically Stable PatchesWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-02-18Full publication date if available
- Authors
-
Yifei Shi, Junwen Huang, Xin Xu, Yifan Zhang, Kai XuList of authors in order
- Landing page
-
https://arxiv.org/abs/2102.09334Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2102.09334Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2102.09334Direct OA link when available
- Concepts
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Pose, Artificial intelligence, Computer science, Object (grammar), Inference, Stability (learning theory), 3D pose estimation, Articulated body pose estimation, Computer vision, Pattern recognition (psychology), Machine learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2023: 2Per-year citation counts (last 5 years)
- References (count)
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52Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.compared | 158 |
| abstract_inverted_index.features | 81 |
| abstract_inverted_index.learning | 77 |
| abstract_inverted_index.methods. | 167 |
| abstract_inverted_index.observed | 28 |
| abstract_inverted_index.performs | 170 |
| abstract_inverted_index.symmetry | 147 |
| abstract_inverted_index.According | 32 |
| abstract_inverted_index.analysis, | 39 |
| abstract_inverted_index.auxiliary | 96 |
| abstract_inverted_index.determine | 51, 110 |
| abstract_inverted_index.extracted | 26 |
| abstract_inverted_index.features. | 85 |
| abstract_inverted_index.geometric | 5, 37, 80 |
| abstract_inverted_index.inference | 20 |
| abstract_inverted_index.introduce | 1 |
| abstract_inverted_index.occlusion | 136 |
| abstract_inverted_index.per-patch | 93 |
| abstract_inverted_index.stability | 6, 38 |
| abstract_inverted_index.benchmarks | 157 |
| abstract_inverted_index.contextual | 84 |
| abstract_inverted_index.depth-only | 162 |
| abstract_inverted_index.estimation | 14 |
| abstract_inverted_index.generalize | 132 |
| abstract_inverted_index.instances. | 139 |
| abstract_inverted_index.relaxation | 100 |
| abstract_inverted_index.subnetwork | 87 |
| abstract_inverted_index.estimation. | 175 |
| abstract_inverted_index.inter-patch | 83 |
| abstract_inverted_index.intra-patch | 79 |
| abstract_inverted_index.prediction: | 105 |
| abstract_inverted_index.ambiguities. | 148 |
| abstract_inverted_index.corresponding | 123 |
| abstract_inverted_index.geometrically | 23, 48, 72 |
| abstract_inverted_index.category-level | 173 |
| abstract_inverted_index.state-of-the-art | 153 |
| abstract_inverted_index.planar/cylindrical | 45 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |