基于多视图投影的半监督手姿态估计算法

Hand Pose Estimation through Semi-Supervised Learning with Multi-View Projection

为解决手姿态估计中标签数据的获取困难问题,该文提出了一种基于多视图投影的半监督学习方法,减少对标记数据的需求。首先,从单张深度图中分割出手部区域,将其投影至3个正交平面;而后,采用编解码模型学习两个投影视图在低维度隐空间中的关联表征;最终,结合标记数据,学习低维度隐空间表征到手姿态三维坐标的回归映射。实验表明,该方法减少了对标记数据的依赖,在NYU手姿态估计数据库上获得了较好的结果。

For hand pose estimation,one immediate problem is to reduce the need for labeled data which is difficult to provide in desired quantity,realism and accuracy.To meet this need,a novel multi-view projection based semi-supervised learning algorithm is proposed.Firstly,3D hand points are extracted from a single depth image without label and projected onto three orthogonal planes.Secondly,an encoder-decoder model is applied to learn the latent representation of two projections.Finally,small amount of labeled data is used to learn a mapping from latent representation to hand joint coordinates.The propose algorithm is evaluated on NYU hand pose estimation dataset,and the experimental results demonstrate the effectiveness and advantages of our proposed algorithm.