Source-linked AI summary
Region Ensemble Network: Improving Convolutional Network for Hand Pose Estimation
Hengkai Guo, Guijin Wang, Xinghao Chen, Cairong Zhang, Fei Qiao, Huazhong Yang
TL;DR
Hand pose estimation from monocular depth images remains challenging, and ConvNets have not clearly improved over traditional methods. The paper introduces REN, which partitions convolutional features into regions and learns to fuse multiple regional regressors end-to-end. REN achieves the best reported performance among state-of-the-art methods on two public datasets.
Problem
Hand pose estimation from monocular depth images is challenging, while ConvNets have not clearly surpassed traditional methods.
Method
REN partitions ConvNet feature maps into regions, applies multiple regressors, and learns their fusion end-to-end for direct 3D coordinate regression.
Results
REN outperforms all state-of-the-art methods on two hand-pose benchmarks.
Takeaways & Limitations
Region-based ensemble fusion significantly improves a single ConvNet without large additional computation overhead.
Abstract
from arXiv · showhide
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.
1. INTRODUCTION
Hand pose estimation remains difficult because depth images exhibit substantial variation, occlusion, and ambiguity, while ConvNets have not clearly surpassed traditional methods. REN addresses this gap with an end-to-end region-based ensemble that improves ConvNet regression and outperforms state-of-the-art methods on two benchmarks.
- Motivation: Hand pose estimation remains challenging because depth images involve view variation, flexible joints, poor quality, self-occlusion, and similar-part confusion.
- Motivation: ConvNets offer strong modeling capacity and end-to-end learning, but sophisticated designs have not shown significant advantages over traditional random-forest methods.
- Contribution: REN directly regresses 3D hand-joint coordinates from monocular depth images using an end-to-end ConvNet architecture.The method trains fully connected regressors on multiple feature regions and combines their outputs as an ensemble.
- Contribution: REN significantly improves the authors’ ConvNet and outperforms all state-of-the-art methods on two challenging hand-pose benchmarks.
2. RELATED WORK
Prior hand-pose work combines ConvNets with heat maps, multi-stage regression, feedback, multiple views, or multiple models. These approaches motivate ensemble strategies but can require complicated architectures or substantial computational resources.
- Hand pose estimation with ConvNets: Existing ConvNet methods estimate hand pose through 2D heat maps, direct 3D regression, multi-stage processing, feedback correction, or multiple view-specific networks.
- Ensemble learning: Multi-model ensemble methods can improve predictions but require large memory and computation, limiting their practicality for applications.
- Multi-branch ensemble methods for ConvNets: Multi-branch ConvNets generalize ensemble learning by fusing different input scales, image cues, or shared-output branches.
3. REGION ENSEMBLE NETWORK (REN)
REN extracts features with a residual ConvNet, divides the feature maps into grid regions, and processes each region with a regressor. Concatenating regional features enables learned end-to-end fusion rather than simple averaging, while the design balances accuracy and efficiency.
- Region Ensemble: REN divides ConvNet feature maps into an n×n grid, applies fully connected regressors to each region, and concatenates their features for learned pose fusion.The authors set n = 2 to balance performance and efficiency.
- Network architecture: The feature extractor uses six 3 × 3 convolution layers, ReLU activations, residual connections, and 12 × 12 × 64 output feature maps.
- Network architecture: Each regressor uses two 2048-dimensional fully connected layers with dropout rate 0.5 and outputs a 3 × J vector of 3D joint coordinates.
- Region Ensemble: Unlike multi-view voting, REN supports regression, trains end-to-end, and learns fusion through fully connected layers on concatenated regional features.
- Implementation: The training pipeline normalizes a resized 96 × 96 depth patch and uses augmentation, SGD, weight decay, and momentum.
4. EXPERIMENTS
Experiments evaluate REN on ICVL and NYU using success-frame comparisons, qualitative examples, runtime, and state-of-the-art benchmarks. REN is reported as the most accurate approach while retaining practical inference speed and avoiding the heavier multi-model setup of some baselines.
- Experimental setup: The experiments use ICVL and NYU, with performance measured by per-joint average Euclidean distance and the percentage of frames below a joint-error threshold.ICVL contains 16 joints, whereas NYU contains 14 joints.
- Self-comparison: All ensemble variants significantly outperform the single network, with region ensemble beating basic bagging and slightly exceeding region bagging on ICVL success frames.Figure 4 presents the self-comparison using percentage of success frames.
- Comparison with state-of-the-arts: REN obtains the best accuracy among evaluated state-of-the-art methods on both ICVL and NYU.The comparison includes several methods across both public datasets.
- Runtime comparison: REN reaches over 3000fps on a single GPU while using nearly the same number of parameters as the Basic Large and Region Bagging networks.The runtime comparison uses an Nvidia Titan X GPU.
- Comparison with state-of-the-arts: On ICVL, REN reduces mean error by 0.63mm versus LSN, a 7.77% relative improvement, and outperforms LSN at the reported threshold pairs.The reported thresholds are (5mm, 15mm) and (20mm, 60mm).
5. CONCLUSION
REN combines region-specific regressors through learned fusion to improve single-ConvNet hand pose estimation without large computational overhead. It outperforms state-of-the-art methods on the ICVL and NYU benchmarks while remaining fast enough for practical use.
- REN divides feature maps into regions and jointly trains multiple regressors whose outputs are fused for pose prediction.
- The ICVL comparison evaluates distance error and percentage of success frames against state-of-the-art methods.
- The NYU comparison uses the same distance-error and success-frame metrics to assess competing methods.
- REN outperforms all state-of-the-art methods on the ICVL and NYU hand-pose benchmarks.