Authors: Xingchen Liu, Piyush Tayal, Jianyuan Wang, Jesus Zarzar, Tom Monnier, Konstantinos Tertikas, Jiali Duan, Antoine Toisoul, Jason Y. Zhang, Natalia Neverova, Andrea Vedaldi, Roman Shapovalov, David Novotny
Abstract: 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.
Source: http://arxiv.org/abs/2501.07574v1
