Summary of Hot3d: Hand and Object Tracking in 3d From Egocentric Multi-view Videos, by Prithviraj Banerjee et al.
HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videos
by Prithviraj Banerjee, Sindi Shkodrani, Pierre Moulon, Shreyas Hampali, Shangchen Han, Fan Zhang, Linguang Zhang, Jade Fountain, Edward Miller, Selen Basol, Richard Newcombe, Robert Wang, Jakob Julian Engel, Tomas Hodan
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 comprises over 833 minutes of RGB/monochrome image streams from two Meta head-mounted devices, capturing 19 subjects interacting with 33 objects in various environments. It includes comprehensive ground-truth annotations, such as 3D poses, hands, cameras, and 3D models. The dataset is designed to facilitate research on multi-view egocentric data for tasks like hand tracking, object pose estimation, and lifting unknown objects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HOT3D is a big dataset that helps computers understand how people interact with objects in the real world. It has lots of pictures taken from different angles by special glasses or a virtual reality headset. The pictures show 19 people doing things like picking up toys, cooking dinner, or just relaxing. The computer can use this information to learn how to track hands and objects in 3D space. This is important because it could help robots and computers work better with humans. |
Keywords
» Artificial intelligence » Object tracking » Pose estimation » Tracking
