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LPPM-Net: Local-aware point processing module based 3D hand pose estimation for point cloud
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DOI：10.1016/j.image.2020.116036.png)
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Abstract
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3D hand pose estimation by taking point cloud as input has been paid more and more attention recently. In this paper, a new module for point cloud processing, named Local-aware Point Processing Module (LPPM), is designed. With the ability to extract local information, it is permutation invariant w.r.t. neighboring points in input point cloud and is an independent module that is easy to be implemented and flexible to construct point cloud network. Based on this module, a LPPM-Net is constructed to estimate 3D hand pose. In order to normalize orientations of the point cloud as well as to maintain diversity properly in a controllable manner, we transform point cloud into an oriented bounding box coordinate system (OBB C.S.) and then rotate it randomly around the principal axis when training. In addition, a simple but effective technique called sampling ensemble is used in the test stage, which compensates for the resolution degradation caused by downsampling and improves the performance without extra parameters. We evaluate the proposed method on three public hand datasets: NYU, ICVL, and MSRA. Results show that our approach has a competitive performance on the three datasets.
Keywords:
Hand pose estimation
Point cloud
Deep learning
Local-aware point processing module
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