4.7 Article Proceedings Paper

Robust 3D Hand Pose Estimation From Single Depth Images Using Multi-View CNNs

Journal

IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 27, Issue 9, Pages 4422-4436

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2018.2834824

Keywords

3D hand pose estimation; convolutional neural networks; multi-view CNNs

Funding

  1. BeingTogether Centre
  2. National Research Foundation, Prime Minister's Office, Singapore under its International Research Centres in Singapore Funding Initiative
  3. Singapore Ministry of Education Academic Research Fund [MOE2015-T2-2-114]
  4. Microsoft Research Asia
  5. University at Buffalo

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Articulated hand pose estimation is one of core technologies in human-computer interaction. Despite the recent progress, most existing methods still cannot achieve satisfactory performance, partly due to the difficulty of the embedded high-dimensional nonlinear regression problem. Most existing data-driven methods directly regress 3D hand pose from 2D depth image, which cannot fully utilize the depth information. In this paper, we propose a novel multi-view convolutional neural network (CNN)-based approach for 3D hand pose estimation. To better exploit 3D information in the depth image, we project the point cloud generated from the query depth image onto multiple views of two projection settings and integrate them for more robust estimation. Multi-view CNNs are trained to learn the mapping from projected images to heat-maps, which reflect probability distributions of joints on each view. These multi-view heat-maps are then fused to estimate the optimal 3D hand pose with learned pose priors, and the unreliable information in multi-view heat-maps is suppressed using a view selection method. Experimental results show that the proposed method is superior to the state-of-the-art methods on two challenging data sets. Furthermore, a cross-data set experiment also validates that our proposed approach has good generalization ability.

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