# Efficient Virtual View Selection for 3D Hand Pose Estimation

arXiv: https://arxiv.org/abs/2203.15458 · HTML: https://mlsanity.com/paper/2203.15458/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map](https://mlsanity.com/paper/1711.07399/) (2017)
- [A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Image](https://mlsanity.com/paper/1908.09999/) (2019)
- [Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Images](https://mlsanity.com/paper/2003.08124/) (2020)
- [Robust 3D Hand Pose Estimation in Single Depth Images: from Single-View CNN to Multi-View CNNs](https://mlsanity.com/paper/1606.07253/) (2016)
- [Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction](https://mlsanity.com/paper/2003.13764/) (2020)
- [Dense 3D Regression for Hand Pose Estimation](https://mlsanity.com/paper/1711.08996/) (2017)
- [AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation](https://mlsanity.com/paper/2007.09590/) (2020)
- [Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images](https://mlsanity.com/paper/1712.03904/) (2017)
- [Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation](https://mlsanity.com/paper/1604.03334/) (2016)
- [Deep Reinforcement Learning for Active Human Pose Estimation](https://mlsanity.com/paper/2001.02024/) (2020)
- [HandAugment: A Simple Data Augmentation Method for Depth-Based 3D Hand Pose Estimation](https://mlsanity.com/paper/2001.00702/) (2020)

## Built on by (most cited first, 6 of 6)

- [3D Vision with Transformers: A Survey](https://mlsanity.com/paper/2208.04309/) (2022)
- [Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wild](https://mlsanity.com/paper/2209.12009/) (2022)
- [HandDiff: 3D Hand Pose Estimation with Diffusion on Image-Point Cloud](https://mlsanity.com/paper/2404.03159/) (2024)
- [Image-free Domain Generalization via CLIP for 3D Hand Pose Estimation](https://mlsanity.com/paper/2210.16788/) (2022)
- [Towards Ubiquitous Intelligent Hand Interaction](https://mlsanity.com/paper/2308.13543/) (2023)
- [KAD-Net: Kinematics-Aware Decoupled Learning for Robust 3D Hand Pose Estimation from a Single Depth Image](https://mlsanity.com/paper/2609.12559/) (2026)

## Abstract (arXiv)

3D hand pose estimation from single depth is a fundamental problem in computer vision, and has wide applications.However, the existing methods still can not achieve satisfactory hand pose estimation results due to view variation and occlusion of human hand. In this paper, we propose a new virtual view selection and fusion module for 3D hand pose estimation from single depth.We propose to automatically select multiple virtual viewpoints for pose estimation and fuse the results of all and find this empirically delivers accurate and robust pose estimation. In order to select most effective virtual views for pose fusion, we evaluate the virtual views based on the confidence of virtual views using a light-weight network via network distillation. Experiments on three main benchmark datasets including NYU, ICVL and Hands2019 demonstrate that our method outperforms the state-of-the-arts on NYU and ICVL, and achieves very competitive performance on Hands2019-Task1, and our proposed virtual view selection and fusion module is both effective for 3D hand pose estimation.