Summary of Mvimgnet2.0: a Larger-scale Dataset Of Multi-view Images, by Xiaoguang Han et al.
MVImgNet2.0: A Larger-scale Dataset of Multi-view Images
by Xiaoguang Han, Yushuang Wu, Luyue Shi, Haolin Liu, Hongjie Liao, Lingteng Qiu, Weihao Yuan, Xiaodong Gu, Zilong Dong, Shuguang Cui
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The MVImgNet2.0 dataset is an expanded version of the original MVImgNet dataset, featuring ~520k real-world objects across 515 categories. This large-scale dataset introduces 3D visual signals through multi-view shooting, bridging the gap between 2D and 3D vision. The updated dataset boasts higher quality features, including 360-degree views, more accurate foreground object masks, improved camera pose estimation, and high-quality dense point clouds. These advancements enable more comprehensive object reconstruction and support downstream applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MVImgNet2.0 is a new database that helps computers recognize objects in three dimensions. It has many images of over 520,000 real-world objects from different angles, making it easier for machines to understand what things look like from all sides. This can help robots and other devices learn how to pick up and move objects more accurately. |
Keywords
» Artificial intelligence » Pose estimation
