论文标题
学会从点云估算3D人类姿势
Learning to Estimate 3D Human Pose from Point Cloud
论文作者
论文摘要
3D姿势估计是计算机视觉中的一个具有挑战性的问题。大多数现有的基于神经网络的方法通过卷积网络（CNN）来解决颜色或深度图像。在本文中，我们研究了从深度图像中估计3D人姿势的任务。与现有的基于CNN的人类姿势估计方法不同，我们通过将点云数据作为输入数据来模拟复杂人类结构的表面来提出一个深人体姿势网络，以进行3D姿势估计。我们首先将3D人体姿势估计从2D深度图像估计到3D点云，并直接预测3D关节位置。我们在两个公共数据集上的实验表明，我们的方法比以前的最新方法达到了更高的准确性。 ITOP和EDAP数据集上的报告结果证明了我们方法对目标任务的有效性。
3D pose estimation is a challenging problem in computer vision. Most of the existing neural-network-based approaches address color or depth images through convolution networks (CNNs). In this paper, we study the task of 3D human pose estimation from depth images. Different from the existing CNN-based human pose estimation method, we propose a deep human pose network for 3D pose estimation by taking the point cloud data as input data to model the surface of complex human structures. We first cast the 3D human pose estimation from 2D depth images to 3D point clouds and directly predict the 3D joint position. Our experiments on two public datasets show that our approach achieves higher accuracy than previous state-of-art methods. The reported results on both ITOP and EVAL datasets demonstrate the effectiveness of our method on the targeted tasks.
