Point2Vec for Self-Supervised Representation Learning on Point Clouds Article Swipe
Karim Abou Zeid
,
Jonas Schult
,
Alexander Hermans
,
Bastian Leibe
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.16570
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.16570
Recently, the self-supervised learning framework data2vec has shown inspiring performance for various modalities using a masked student-teacher approach. However, it remains open whether such a framework generalizes to the unique challenges of 3D point clouds. To answer this question, we extend data2vec to the point cloud domain and report encouraging results on several downstream tasks. In an in-depth analysis, we discover that the leakage of positional information reveals the overall object shape to the student even under heavy masking and thus hampers data2vec to learn strong representations for point clouds. We address this 3D-specific shortcoming by proposing point2vec, which unleashes the full potential of data2vec-like pre-training on point clouds. Our experiments show that point2vec outperforms other self-supervised methods on shape classification and few-shot learning on ModelNet40 and ScanObjectNN, while achieving competitive results on part segmentation on ShapeNetParts. These results suggest that the learned representations are strong and transferable, highlighting point2vec as a promising direction for self-supervised learning of point cloud representations.
Related Topics
Concepts
Point cloud
Computer science
Segmentation
Point (geometry)
Representation (politics)
Masking (illustration)
Artificial intelligence
Object (grammar)
Feature learning
Machine learning
Domain (mathematical analysis)
Modalities
Mathematics
Sociology
Geometry
Law
Visual arts
Mathematical analysis
Politics
Political science
Art
Social science
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2303.16570
- https://arxiv.org/pdf/2303.16570
- OA Status
- green
- Cited By
- 8
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4361806775
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4361806775Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2303.16570Digital Object Identifier
- Title
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Point2Vec for Self-Supervised Representation Learning on Point CloudsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-29Full publication date if available
- Authors
-
Karim Abou Zeid, Jonas Schult, Alexander Hermans, Bastian LeibeList of authors in order
- Landing page
-
https://arxiv.org/abs/2303.16570Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2303.16570Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2303.16570Direct OA link when available
- Concepts
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Point cloud, Computer science, Segmentation, Point (geometry), Representation (politics), Masking (illustration), Artificial intelligence, Object (grammar), Feature learning, Machine learning, Domain (mathematical analysis), Modalities, Mathematics, Sociology, Geometry, Law, Visual arts, Mathematical analysis, Politics, Political science, Art, Social scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 3Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.several | 52 |
| abstract_inverted_index.student | 74 |
| abstract_inverted_index.suggest | 139 |
| abstract_inverted_index.various | 11 |
| abstract_inverted_index.whether | 22 |
| abstract_inverted_index.However, | 18 |
| abstract_inverted_index.data2vec | 5, 41, 82 |
| abstract_inverted_index.discover | 60 |
| abstract_inverted_index.few-shot | 122 |
| abstract_inverted_index.in-depth | 57 |
| abstract_inverted_index.learning | 3, 123, 156 |
| abstract_inverted_index.Recently, | 0 |
| abstract_inverted_index.achieving | 129 |
| abstract_inverted_index.analysis, | 58 |
| abstract_inverted_index.approach. | 17 |
| abstract_inverted_index.direction | 153 |
| abstract_inverted_index.framework | 4, 25 |
| abstract_inverted_index.inspiring | 8 |
| abstract_inverted_index.point2vec | 113, 149 |
| abstract_inverted_index.potential | 102 |
| abstract_inverted_index.promising | 152 |
| abstract_inverted_index.proposing | 96 |
| abstract_inverted_index.question, | 38 |
| abstract_inverted_index.unleashes | 99 |
| abstract_inverted_index.ModelNet40 | 125 |
| abstract_inverted_index.challenges | 30 |
| abstract_inverted_index.downstream | 53 |
| abstract_inverted_index.modalities | 12 |
| abstract_inverted_index.point2vec, | 97 |
| abstract_inverted_index.positional | 65 |
| abstract_inverted_index.3D-specific | 93 |
| abstract_inverted_index.competitive | 130 |
| abstract_inverted_index.encouraging | 49 |
| abstract_inverted_index.experiments | 110 |
| abstract_inverted_index.generalizes | 26 |
| abstract_inverted_index.information | 66 |
| abstract_inverted_index.outperforms | 114 |
| abstract_inverted_index.performance | 9 |
| abstract_inverted_index.shortcoming | 94 |
| abstract_inverted_index.highlighting | 148 |
| abstract_inverted_index.pre-training | 105 |
| abstract_inverted_index.segmentation | 134 |
| abstract_inverted_index.ScanObjectNN, | 127 |
| abstract_inverted_index.data2vec-like | 104 |
| abstract_inverted_index.transferable, | 147 |
| abstract_inverted_index.ShapeNetParts. | 136 |
| abstract_inverted_index.classification | 120 |
| abstract_inverted_index.representations | 86, 143 |
| abstract_inverted_index.self-supervised | 2, 116, 155 |
| abstract_inverted_index.student-teacher | 16 |
| abstract_inverted_index.representations. | 160 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |