Papers › Region Ensemble Network: Improving Convolutional Network for Hand Pose Estimation

Region Ensemble Network: Improving Convolutional Network for Hand Pose Estimation

8 Feb 2017arXiv:1702.02447archive 2025-07-28

Hengkai Guo, Guijin Wang, Xinghao Chen, Cairong Zhang, Fei Qiao, Huazhong Yang

Hand pose estimation from monocular depth images is an important and challenging problem for human-computer interaction. Recently deep convolutional networks (ConvNet) with sophisticated design have been employed to address it, but the improvement over traditional methods is not so apparent. To promote the performance of directly 3D coordinate regression, we propose a tree-structured Region Ensemble Network (REN), which partitions the convolution outputs into regions and integrates the results from multiple regressors on each regions. Compared with multi-model ensemble, our model is completely end-to-end training. The experimental results demonstrate that our approach achieves the best performance among state-of-the-arts on two public datasets.

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Tasks

Hand Pose EstimationPose Estimationregression

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Hand Pose Estimation ICVL Hands REN Average 3D Error 7.5 #14 of 15 Archive leaderboard report
Hand Pose Estimation MSRA Hands REN Average 3D Error 9.8 #11 of 11 Archive leaderboard report
Hand Pose Estimation NYU Hands REN Average 3D Error 12.7 #16 of 17 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Convolution

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