title: UnCommon Objects in 3D
summary: 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.
paper_authors._id: 6785e58ffea9f9a51055f21b
paper_authors.name: Xingchen Liu
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paper_authors.fullname: AlexLiu
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paper_authors.name: Piyush Tayal
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paper_authors.name: Jianyuan Wang
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paper_authors.fullname: Jianyuan Wang
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paper_authors.name: Jesus Zarzar
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paper_authors.fullname: Jesus Zarzar
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paper_authors.name: Tom Monnier
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paper_authors.name: Konstantinos Tertikas
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paper_authors.name: Jiali Duan
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paper_authors.user: Jiali
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paper_authors.name: Antoine Toisoul
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paper_authors.name: Jason Y. Zhang
paper_authors._id: 6785e58ffea9f9a51055f224
paper_authors.name: Natalia Neverova
paper_authors._id: 6785e58ffea9f9a51055f225
paper_authors.name: Andrea Vedaldi
paper_authors._id: 6785e58ffea9f9a51055f226
paper_authors.name: Roman Shapovalov
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paper_authors.statusLastChangedAt: 2025-01-14T09:51:06.525Z
paper_authors._id: 63e6d41db6a40bf941e4980c
paper_authors.avatarUrl: /avatars/1483f177c9ed6bbe96cbce5a1fa98ceb.svg
paper_authors.fullname: Roman Shapovalov
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paper_authors.user: EarlGr
paper_authors._id: 6785e58ffea9f9a51055f227
paper_authors.name: David Novotny
paper_title: UnCommon Objects in 3D
paper_summary: 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.
paper_ai_summary: The introduction of uCO3D, a comprehensive 3D dataset with high-resolution videos and detailed annotations, enhances the performance of 3D deep learning and generative AI models.
paper_ai_keywords: object-centric dataset 3D annotations full-360° coverage 3D camera poses depth maps sparse point clouds 3D Gaussian Splat reconstruction