# End-to-End 3D Hand Pose Estimation from Stereo Cameras

arXiv: https://arxiv.org/abs/2206.01384 · HTML: https://mlsanity.com/paper/2206.01384/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 35 references that are in the map.
- First posted on arXiv 3 Jun 2022.

## Builds on (most cited first, 19 of 19)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [AMC: AutoML for Model Compression and Acceleration on Mobile Devices](https://mlsanity.com/paper/1802.03494/) (2018)
- [End-to-End Learning of Geometry and Context for Deep Stereo Regression](https://mlsanity.com/paper/1703.04309/) (2017)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [Learning to Estimate 3D Hand Pose from Single RGB Images](https://mlsanity.com/paper/1705.01389/) (2017)
- [3D Hand Shape and Pose Estimation from a Single RGB Image](https://mlsanity.com/paper/1903.00812/) (2019)
- [GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB](https://mlsanity.com/paper/1712.01057/) (2017)
- [Capturing Hands in Action using Discriminative Salient Points and Physics Simulation](https://mlsanity.com/paper/1506.02178/) (2015)
- [Cross-modal Deep Variational Hand Pose Estimation](https://mlsanity.com/paper/1803.11404/) (2018)
- [Using a single RGB frame for real time 3D hand pose estimation in the wild](https://mlsanity.com/paper/1712.03866/) (2017)
- [Single View Stereo Matching](https://mlsanity.com/paper/1803.02612/) (2018)
- [3D Hand Pose Tracking and Estimation Using Stereo Matching](https://mlsanity.com/paper/1610.07214/) (2016)
- [Training a Feedback Loop for Hand Pose Estimation](https://mlsanity.com/paper/1609.09698/) (2016)
- [Region Ensemble Network: Improving Convolutional Network for Hand Pose Estimation](https://mlsanity.com/paper/1702.02447/) (2017)
- [RGB-based 3D Hand Pose Estimation via Privileged Learning with Depth Images](https://mlsanity.com/paper/1811.07376/) (2018)
- [Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation](https://mlsanity.com/paper/1604.03334/) (2016)
- [Back to RGB: 3D tracking of hands and hand-object interactions based on short-baseline stereo](https://mlsanity.com/paper/1705.05301/) (2017)

## Built on by (most cited first, 1 of 1)

- [ESTHER: Egocentric Stereo Hand Estimation and Reconstruction in the Wild](https://mlsanity.com/paper/2609.34817/) (2026)

## Abstract (arXiv)

This work proposes an end-to-end approach to estimate full 3D hand pose from stereo cameras. Most existing methods of estimating hand pose from stereo cameras apply stereo matching to obtain depth map and use depth-based solution to estimate hand pose. In contrast, we propose to bypass the stereo matching and directly estimate the 3D hand pose from the stereo image pairs. The proposed neural network architecture extends from any keypoint predictor to estimate the sparse disparity of the hand joints. In order to effectively train the model, we propose a large scale synthetic dataset that is composed of stereo image pairs and ground truth 3D hand pose annotations. Experiments show that the proposed approach outperforms the existing methods based on the stereo depth.