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.

近年来，以点云为输入的3D手势估计越来越受到重视。本文设计了一种新的点云处理模块，称为本地感知点处理模块（LPPM）。具有提取局部信息的能力，它是输入点云中相邻点的置换不变性，并且是一个易于实现且灵活构建点云网络的独立模块。基于此模块，LPPM-Net用于估计3D手势。为了使点云的方向正常化并以可控的方式适当地保持多样性，我们将点云转换为定向的边界框坐标系（OBB CS），然后在训练时围绕主轴随机旋转。此外，在测试阶段使用了一种简单但有效的技术，称为采样合奏，该技术可以补偿由下采样引起的分辨率下降，并且无需额外参数即可提高性能。我们在三个公共手数据集上评估了所提出的方法：NYU，ICVL和MSRA。结果表明，我们的方法在这三个数据集上均具有竞争优势。