Spatial-aware stacked regression network for real-time 3D hand pose estimation
Neurocomputing, ISSN: 0925-2312, Vol: 437, Page: 42-57
2021
- 22Citations
- 34Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
Making full use of the spatial information of the depth data is crucial for 3D hand pose estimation from a single depth image. In this paper, we propose a Spatial-aware Stacked Regression Network (SSRN) for fast, robust and accurate 3D hand pose estimation from a single depth image. By adopting a differentiable pose re-parameterization process, our method efficiently encodes the pose-dependent 3D spatial structure of the depth data as spatial-aware representations. Taking such spatial-aware representations as inputs, the stacked regression network utilizes multi-joint spatial context and the 3D spatial relationship between the estimated pose and the depth data to predict a refined hand pose. To further improve the estimation accuracy, we adopt a spatial attention mechanism to reduce the influence of irrelevant features for pose regression. In order to improve the speed of the network, we propose a cross-stage self-distillation mechanism to distill knowledge within the network itself. Experiments on four datasets show that our proposed method achieves state-of-the-art accuracy with high running speed around 330 FPS on a single GPU and 35 FPS on a single CPU.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0925231221000667; http://dx.doi.org/10.1016/j.neucom.2021.01.045; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85100387890&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0925231221000667; https://dx.doi.org/10.1016/j.neucom.2021.01.045
Elsevier BV
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