UnCommon Objects in 3D
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866915101254090752 |
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| author | Liu, Xingchen Tayal, Piyush Wang, Jianyuan Zarzar, Jesus Monnier, Tom Tertikas, Konstantinos Duan, Jiali Toisoul, Antoine Zhang, Jason Y. Neverova, Natalia Vedaldi, Andrea Shapovalov, Roman Novotny, David |
| author_facet | Liu, Xingchen Tayal, Piyush Wang, Jianyuan Zarzar, Jesus Monnier, Tom Tertikas, Konstantinos Duan, Jiali Toisoul, Antoine Zhang, Jason Y. Neverova, Natalia Vedaldi, Andrea Shapovalov, Roman Novotny, David |
| contents | We introduce Uncommon Objects in 3D (uCO3D), a new object-centric dataset for 3D deep learning and 3D generative AI. uCO3D is the largest publicly-available collection of high-resolution videos of objects with 3D annotations that ensures full-360$^{\circ}$ coverage. uCO3D is significantly more diverse than MVImgNet and CO3Dv2, covering more than 1,000 object categories. It is also of higher quality, due to extensive quality checks of both the collected videos and the 3D annotations. Similar to analogous datasets, uCO3D contains annotations for 3D camera poses, depth maps and sparse point clouds. In addition, each object is equipped with a caption and a 3D Gaussian Splat reconstruction. We train several large 3D models on MVImgNet, CO3Dv2, and uCO3D and obtain superior results using the latter, showing that uCO3D is better for learning applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07574 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UnCommon Objects in 3D Liu, Xingchen Tayal, Piyush Wang, Jianyuan Zarzar, Jesus Monnier, Tom Tertikas, Konstantinos Duan, Jiali Toisoul, Antoine Zhang, Jason Y. Neverova, Natalia Vedaldi, Andrea Shapovalov, Roman Novotny, David Computer Vision and Pattern Recognition Artificial Intelligence Graphics We introduce Uncommon Objects in 3D (uCO3D), a new object-centric dataset for 3D deep learning and 3D generative AI. uCO3D is the largest publicly-available collection of high-resolution videos of objects with 3D annotations that ensures full-360$^{\circ}$ coverage. uCO3D is significantly more diverse than MVImgNet and CO3Dv2, covering more than 1,000 object categories. It is also of higher quality, due to extensive quality checks of both the collected videos and the 3D annotations. Similar to analogous datasets, uCO3D contains annotations for 3D camera poses, depth maps and sparse point clouds. In addition, each object is equipped with a caption and a 3D Gaussian Splat reconstruction. We train several large 3D models on MVImgNet, CO3Dv2, and uCO3D and obtain superior results using the latter, showing that uCO3D is better for learning applications. |
| title | UnCommon Objects in 3D |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Graphics |
| url | https://arxiv.org/abs/2501.07574 |