Summary of Dor3d-net: Dense Ordinal Regression Network For 3d Hand Pose Estimation, by Yamin Mao et al.
DOR3D-Net: Dense Ordinal Regression Network for 3D Hand Pose Estimation
by Yamin Mao, Zhihua Liu, Weiming Li, SoonYong Cho, Qiang Wang, Xiaoshuai Hao
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a novel approach to 3D hand pose estimation using dense ordinal regression, which reformulates the task as a series of binary classifications with ordinal constraints. The proposed Dense Ordinal Regression 3D Pose Network (DOR3D-Net) outperforms state-of-the-art methods on public datasets ICVL, MSRA, NYU, and HANDS2017. By decomposing offset value regression into sub-tasks, the method reduces noise and improves accuracy. The network is trained end-to-end with joint regression loss and ordinal regression loss, demonstrating its effectiveness in estimating 3D hand poses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand how our hands move in space. It’s a tricky task because there can be lots of errors due to noise or mistakes. The researchers came up with a new way to solve this problem called dense ordinal regression. This method breaks down the task into smaller, easier-to-solve problems and then puts the answers together to get an accurate estimate of how our hands are moving. They tested their approach on several public datasets and found that it works better than other methods. |
Keywords
» Artificial intelligence » Pose estimation » Regression
