Traditional approaches for learning 3D object categories have been
predominantly trained and evaluated on synthetic datasets due to the
unavailability of real 3D-annotated category-centric data. Our main goal is to
facilitate advances in this field by collecting real-world data in a magnitude
similar to the existing synthetic counterparts. The principal contribution of
this work is thus a large-scale dataset, called Common Objects in 3D, with real
multi-view images of object categories annotated with camera poses and ground
truth 3D point clouds. The dataset contains a total of 1.5 million frames from
nearly 19,000 videos capturing objects from 50 MS-COCO categories and, as such,
it is significantly larger than alternatives both in terms of the number of
categories and objects. We exploit this new dataset to conduct one of the first
large-scale "in-the-wild" evaluations of several new-view-synthesis and
category-centric 3D reconstruction methods. Finally, we contribute NerFormer -
a novel neural rendering method that leverages the powerful Transformer to
reconstruct an object given a small number of its views. The CO3D dataset is
available at this https URL .

该研究提供了一种名为 CO3D 的大规模数据集，其中包含了 50 种 MS-COCO 类别的近 19,000 个视频，共计 1.5 百万帧真实的多视角图像，带有相机姿态和地面真实 3D 点云的注释，并利用该数据集进行了新视角综合和类别为中心的 3D 重建方法的大规模 “野外” 评估，并提出了一种基于 Transformer 的新型神经渲染方法 NerFormer，可根据物体的少数视角重建整个物体。