Recently, 3D input data based hand pose estimation methods have shown state-of-the-art performance, because 3D data capture more spatial information than the depth image. Whereas 3D voxel-based methods need a large amount of memory, PointNet based methods need tedious preprocessing steps such as K-nearest neighbour search for each point. In this paper, we present a novel deep learning hand pose estimation method for an unordered point cloud. Our method takes 1024 3D points as input and does not require additional information. We use Permutation Equivariant Layer (PEL) as the basic element, where a residual network version of PEL is proposed for the hand pose estimation task. Furthermore, we propose a voting based scheme to merge information from individual points to the final pose output. In addition to the pose estimation task, the voting-based scheme can also provide point cloud segmentation result without ground-truth for segmentation. We evaluate our method on both NYU dataset and the Hands2017Challenge dataset. Our method outperforms recent state-of-the-art methods, where our pose accuracy is currently the best for the Hands2017Challenge dataset.

本研究提出了一种用于无序点云的新型深度学习手势姿态估计方法，采用Permutation Equivariant Layer (PEL)作为基本元素，通过投票的方式将各点信息合并到最终姿态输出中，并在NYU数据集和Hands2017Challenge数据集上验证了其性能优于最近的最先进方法，成为Hands2017Challenge数据集中准确性最高的方法。

基于点姿投票和残差置换等变层的手部姿态估计