基于改进PointNet网络的三维手姿估计方法  

3D hand pose estimation method based on improved PointNet

作　　者：马利[1] 金珊杉 牛斌[1] Ma Li;Jin Shanshan;Niu Bin(College of Information,Liaoning University,Shenyang 110036,China)

机构地区：[1]辽宁大学信息学院,沈阳110036

出　　处：《计算机应用研究》2020年第10期3188-3192,共5页Application Research of Computers

基　　金：2017年辽宁省科技厅博士科研启动基金指导计划资助项目(20170520276)。

摘　　要：针对单幅深度图像三维手姿估计中由于手部复杂结构捕捉困难导致的精度低和鲁棒性较差的问题,提出一种基于改进PointNet网络的三维手姿估计方法。该方法首先采用边界框定位网络预测三维边界框,从而准确裁剪手部区域。然后将手部深度图像表示为点云,模拟手部可见表面,有效地利用深度图像中的三维信息。最后将手部点云数据输入改进的PointNet网络,准确地进行三维手姿估计。改进的PointNet网络通过引入跳跃连接,充分利用不同层次的特征,更好地捕捉手部的复杂结构。在NYU手姿数据集上进行验证,实验结果表明,提出的方法优于现有的大部分方法,并且网络结构简单、易于训练,运行速度快。Due to the difficulty of capturing complex structure of hands,3D hand pose estimation in single depth image still suffers from the issues of low accuracy and poor robustness.In order to solve these problems,this paper proposed a 3D hand pose estimation method based on improved PointNet.The method firstly used a bounding box localization network to predict a 3D bounding box,and thereby accurately cropped the hand region.Then,it represented the depth image of the hand with point cloud.The point cloud that used to model the visible surface of the hand,could effectively utilize the 3D information in the depth image.At last,the hand point cloud was input into the improved PointNet to accurately estimate 3D hand pose.By introducing a jump connection,the improved PointNet made full use of the features of different levels,so it could capture the complex structure of the hand.The experimental results on NYU hand pose dataset show that the proposed method outperforms most of the existing methods,and the network is simple in structure,easy to train,and fast to run.

关 键 词：三维手姿估计 单幅深度图像 PointNet 神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]