In this paper, a method is proposed to improve the accuracy of 3D hand pose estimation. The existing methods make poor use of the depth information of hand joints and have difficulties of estimating the 3D coordinates accurately. To solve this problem, a method that utilizing the information between adjacent joints of each finger is proposed to estimate the depth coordinates of joints. In order to make full use of 2D information for depth estimation, this paper divides hand pose estimation into two sub-tasks (2D hand joints estimation and depth estimation). In depth estimation, a multi-stage network is proposed. We first estimate the depth of a part of hand joints, and then with the help of it and 2D information, the depth coordinates of adjacent joints can be well estimated. The method proposed in this paper has been proved to be effective on three public hand pose datasets through Self-comparisons. Compared with the methods that based on 2D CNN, our method achieves state-of-the-art performance on ICVL and NYU datasets, and also has a good result on MSRA dataset.

本文提出了一种提高3D手势估计精度的方法。现有方法很少利用手关节的深度信息，并且难以准确地估计3D坐标。为了解决这个问题，提出了一种利用每个手指的相邻关节之间的信息来估计关节的深度坐标的方法。为了充分利用2D信息进行深度估计，本文将手姿势估计分为两个子任务（2D手关节估计和深度估计）。在深度估计中，提出了一种多级网络。我们首先估计手部关节的一部分的深度，然后借助它和2D信息，可以很好地估计相邻关节的深度坐标。通过自我比较，证明了本文提出的方法对三种公共手部姿势数据集均有效。与基于2D CNN的方法相比，我们的方法在ICVL和NYU数据集上具有最先进的性能，并且在MSRA数据集上也具有良好的效果。