UnCommon Objects in 3D
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.
6785e58ffea9f9a51055f21b
Xingchen Liu
admin_assigned
2025-01-17T16:47:38.600Z
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AlexLiu
user
DrAlexLiu
6785e58ffea9f9a51055f21c
Piyush Tayal
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Jianyuan Wang
admin_assigned
2025-01-14T09:48:59.566Z
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Jianyuan Wang
user
JianyuanWang
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Jesus Zarzar
admin_assigned
2025-01-14T09:49:05.230Z
66e844b28b2ed9cd2649285c
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Jesus Zarzar
user
zarzarj
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Tom Monnier
6785e58ffea9f9a51055f220
Konstantinos Tertikas
6785e58ffea9f9a51055f221
Jiali Duan
admin_assigned
2025-01-14T09:49:24.788Z
61feaf91fe53002dadb25586
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Duan
user
Jiali
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Antoine Toisoul
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Jason Y. Zhang
6785e58ffea9f9a51055f224
Natalia Neverova
6785e58ffea9f9a51055f225
Andrea Vedaldi
6785e58ffea9f9a51055f226
Roman Shapovalov
admin_assigned
2025-01-14T09:51:06.525Z
63e6d41db6a40bf941e4980c
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Roman Shapovalov
user
EarlGr
6785e58ffea9f9a51055f227
David Novotny
UnCommon Objects in 3D
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.
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.
object-centric dataset 3D annotations full-360° coverage 3D camera poses depth maps sparse point clouds 3D Gaussian Splat reconstruction