# Hand Segmentation for Hand-Object Interaction from Depth map

arXiv: https://arxiv.org/abs/1603.02345 · HTML: https://mlsanity.com/paper/1603.02345/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2016.

## Built on by (most cited first, 5 of 5)

- [Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-pixel Part Segmentation](https://mlsanity.com/paper/2107.00434/) (2021)
- [TriHorn-Net: A Model for Accurate Depth-Based 3D Hand Pose Estimation](https://mlsanity.com/paper/2206.07117/) (2022)
- [Benchmarking 2D Egocentric Hand Pose Datasets](https://mlsanity.com/paper/2409.07337/) (2024)
- [The Collection of a Human Robot Collaboration Dataset for Cooperative Assembly in Glovebox Environments](https://mlsanity.com/paper/2407.14649/) (2024)
- [Semi-supervised 3D Hand-Object Pose Estimation via Pose Dictionary Learning](https://mlsanity.com/paper/2107.07676/) (2021)

## Abstract (arXiv)

Hand segmentation for hand-object interaction is a necessary preprocessing step in many applications such as augmented reality, medical application, and human-robot interaction. However, typical methods are based on color information which is not robust to objects with skin color, skin pigment difference, and light condition variations. Thus, we propose hand segmentation method for hand-object interaction using only a depth map. It is challenging because of the small depth difference between a hand and objects during an interaction. To overcome this challenge, we propose the two-stage random decision forest (RDF) method consisting of detecting hands and segmenting hands. To validate the proposed method, we demonstrate results on the publicly available dataset of hand segmentation for hand-object interaction. The proposed method achieves high accuracy in short processing time comparing to the other state-of-the-art methods.