RGBD Objects in the Wild: Scaling Real-World 3D Object Learning from RGB-D Videos
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911970169454592 |
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| author | Xia, Hongchi Fu, Yang Liu, Sifei Wang, Xiaolong |
| author_facet | Xia, Hongchi Fu, Yang Liu, Sifei Wang, Xiaolong |
| contents | 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 https://wildrgbd.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12592 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RGBD Objects in the Wild: Scaling Real-World 3D Object Learning from RGB-D Videos Xia, Hongchi Fu, Yang Liu, Sifei Wang, Xiaolong Computer Vision and Pattern Recognition 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 https://wildrgbd.github.io/. |
| title | RGBD Objects in the Wild: Scaling Real-World 3D Object Learning from RGB-D Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.12592 |