# Robust 3D Hand Pose Estimation in Single Depth Images: from Single-View CNN to Multi-View CNNs

arXiv: https://arxiv.org/abs/1606.07253 · HTML: https://mlsanity.com/paper/1606.07253/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2016.

## Built on by (most cited first, 29 of 29)

- [WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild](https://mlsanity.com/paper/2409.12259/) (2024)
- [HaWoR: World-Space Hand Motion Reconstruction from Egocentric Videos](https://mlsanity.com/paper/2501.02973/) (2025)
- [Model-based 3D Hand Reconstruction via Self-Supervised Learning](https://mlsanity.com/paper/2103.11703/) (2021)
- [A Skeleton-Driven Neural Occupancy Representation for Articulated Hands](https://mlsanity.com/paper/2109.11399/) (2021)
- [Hand Image Understanding via Deep Multi-Task Learning](https://mlsanity.com/paper/2107.11646/) (2021)
- [SOMA: Solving Optical Marker-Based MoCap Automatically](https://mlsanity.com/paper/2110.04431/) (2021)
- [Consistent 3D Hand Reconstruction in Video via self-supervised Learning](https://mlsanity.com/paper/2201.09548/) (2022)
- [Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements](https://mlsanity.com/paper/2111.00763/) (2021)
- [Im2Hands: Learning Attentive Implicit Representation of Interacting Two-Hand Shapes](https://mlsanity.com/paper/2302.14348/) (2023)
- [emg2pose: A Large and Diverse Benchmark for Surface Electromyographic Hand Pose Estimation](https://mlsanity.com/paper/2412.02725/) (2024)
- [HandFoldingNet: A 3D Hand Pose Estimation Network Using Multiscale-Feature Guided Folding of a 2D Hand Skeleton](https://mlsanity.com/paper/2108.05545/) (2021)
- [HandTailor: Towards High-Precision Monocular 3D Hand Recovery](https://mlsanity.com/paper/2102.09244/) (2021)
- [HandDiff: 3D Hand Pose Estimation with Diffusion on Image-Point Cloud](https://mlsanity.com/paper/2404.03159/) (2024)
- [FourierHandFlow: Neural 4D Hand Representation Using Fourier Query Flow](https://mlsanity.com/paper/2307.08100/) (2023)
- [Learning Multi-view Anomaly Detection with Efficient Adaptive Selection](https://mlsanity.com/paper/2407.11935/) (2024)
- [Neuromorphic High-Frequency 3D Dancing Pose Estimation in Dynamic Environment](https://mlsanity.com/paper/2301.06648/) (2023)
- [HaMuCo: Hand Pose Estimation via Multiview Collaborative Self-Supervised Learning](https://mlsanity.com/paper/2302.00988/) (2023)
- [Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimation](https://mlsanity.com/paper/2112.06389/) (2021)
- [Efficient Virtual View Selection for 3D Hand Pose Estimation](https://mlsanity.com/paper/2203.15458/) (2022)
- [RealisticHands: A Hybrid Model for 3D Hand Reconstruction](https://mlsanity.com/paper/2108.13995/) (2021)
- [EgoHandICL: Egocentric 3D Hand Reconstruction with In-Context Learning](https://mlsanity.com/paper/2601.19850/) (2026)
- [AirSketch: Generative Motion to Sketch](https://mlsanity.com/paper/2407.08906/) (2024)
- [UniHand: A Unified Model for Diverse Controlled 4D Hand Motion Modeling](https://mlsanity.com/paper/2602.21631/) (2026)
- [HandDAGT: A Denoising Adaptive Graph Transformer for 3D Hand Pose Estimation](https://mlsanity.com/paper/2407.20542/) (2024)
- [Serial-parallel Multi-Scale Feature Fusion for Anatomy-Oriented Hand Joint Detection](https://mlsanity.com/paper/2102.09757/) (2021)
- [LG-Hand: Advancing 3D Hand Pose Estimation with Locally and Globally Kinematic Knowledge](https://mlsanity.com/paper/2211.03151/) (2022)
- [HandMCM: Multi-modal Point Cloud-based Correspondence State Space Model for 3D Hand Pose Estimation](https://mlsanity.com/paper/2602.01586/) (2026)
- [HandMvNet: Real-Time 3D Hand Pose Estimation Using Multi-View Cross-Attention Fusion](https://mlsanity.com/paper/2608.20093/) (2026)
- [EventEgoHands++: Event-based Egocentric 3D Hand Mesh Reconstruction with Real Dataset](https://mlsanity.com/paper/2609.17189/) (2026)

## Abstract (arXiv)

Articulated hand pose estimation plays an important role in human-computer interaction. Despite the recent progress, the accuracy of existing methods is still not satisfactory, partially due to the difficulty of embedded high-dimensional and non-linear regression problem. Different from the existing discriminative methods that regress for the hand pose with a single depth image, we propose to first project the query depth image onto three orthogonal planes and utilize these multi-view projections to regress for 2D heat-maps which estimate the joint positions on each plane. These multi-view heat-maps are then fused to produce final 3D hand pose estimation with learned pose priors. Experiments show that the proposed method largely outperforms state-of-the-art on a challenging dataset. Moreover, a cross-dataset experiment also demonstrates the good generalization ability of the proposed method.