3D中的非常见物体

UnCommon Objects in 3D

January 13, 2025

作者: 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

cs.AI

摘要

我们介绍了Uncommon Objects in 3D（uCO3D），这是一个新的面向对象的数据集，用于3D深度学习和3D生成人工智能。uCO3D是最大的公开高分辨率对象视频集，具有3D注释，确保全方位360度覆盖。与MVImgNet和CO3Dv2相比，uCO3D具有更大的多样性，涵盖了1000多个对象类别。由于对收集的视频和3D注释进行了广泛的质量检查，因此质量更高。类似于类似的数据集，uCO3D包含了3D摄像机姿势、深度图和稀疏点云的注释。此外，每个对象都配有标题和3D高斯斑点重建。我们在MVImgNet、CO3Dv2和uCO3D上训练了几个大型3D模型，并发现使用后者可以获得更好的结果，表明uCO3D更适合学习应用。

English

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.