Summary of Introducing Hot3d: An Egocentric Dataset For 3d Hand and Object Tracking, by Prithviraj Banerjee et al.
Introducing HOT3D: An Egocentric Dataset for 3D Hand and Object Tracking
by Prithviraj Banerjee, Sindi Shkodrani, Pierre Moulon, Shreyas Hampali, Fan Zhang, Jade Fountain, Edward Miller, Selen Basol, Richard Newcombe, Robert Wang, Jakob Julian Engel, Tomas Hodan
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: None
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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 paper introduces HOT3D, a large-scale dataset for egocentric hand and object tracking in 3D. The dataset contains over 833 minutes of multi-view RGB/monochrome image streams showing 19 subjects interacting with 33 diverse rigid objects, as well as comprehensive ground truth annotations including 3D poses of objects, hands, and cameras. The dataset includes various scenarios resembling typical actions in a kitchen, office, and living room environment. The authors aim to accelerate research on egocentric hand-object interaction by making the HOT3D dataset publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shares a new dataset called HOT3D that helps machines understand how people interact with objects using their hands. It has lots of pictures and information about what’s happening in different scenes, like a kitchen or office. This data can help scientists develop better computers that can see and understand our actions. |
Keywords
» Artificial intelligence » Object tracking
