标题： 3D中的不常见对象 显示英文标题

标题： UnCommon Objects in 3D

摘要： 我们引入了Uncommon Objects in 3D (uCO3D)，这是一个新的以物体为中心的3D深度学习和3D生成AI数据集。uCO3D是目前公开可用的最高分辨率物体视频集合，具有完整的360$^{\circ}$覆盖。uCO3D比MVImgNet和CO3Dv2要多样化得多，涵盖了超过1000个物体类别。此外，由于对收集的视频和3D注释进行了广泛的品质检查，其质量也更高。类似于类似的数据集，uCO3D包含3D相机姿态、深度图和稀疏点云的注释。此外，每个物体都配有标题和3D高斯点云重建。我们在MVImgNet、CO3Dv2和uCO3D上训练了多个大型3D模型，并使用后者获得了更优的结果，这表明uCO3D在学习应用中表现更好。 显示英文摘要

摘要： 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.