We introduce a new RGB-D object dataset captured in the wild called
WildRGB-D. Unlike most existing real-world object-centric datasets which only
come with RGB capturing, the direct capture of the depth channel allows better
3D annotations and broader downstream applications. WildRGB-D comprises
large-scale category-level RGB-D object videos, which are taken using an iPhone
to go around the objects in 360 degrees. It contains around 8500 recorded
objects and nearly 20000 RGB-D videos across 46 common object categories. These
videos are taken with diverse cluttered backgrounds with three setups to cover
as many real-world scenarios as possible: (i) a single object in one video;
(ii) multiple objects in one video; and (iii) an object with a static hand in
one video. The dataset is annotated with object masks, real-world scale camera
poses, and reconstructed aggregated point clouds from RGBD videos. We benchmark
four tasks with WildRGB-D including novel view synthesis, camera pose
estimation, object 6d pose estimation, and object surface reconstruction. Our
experiments show that the large-scale capture of RGB-D objects provides a large
potential to advance 3D object learning. Our project page is
this https URL

我们介绍了一个名为 WildRGB-D 的新型 RGB-D 物体数据集，该数据集以自然环境中的实际采集为基础，与其他数据集相比，它的直接深度采集使得 3D 标注和下游应用更加准确和广泛。