非刚性三维点云配准方法

Non-rigid 3D Point Cloud Registration Method

非刚性人体重建的关键步骤为三维点云的非刚性配准,本文针对点云的非刚性配准方法展开研究。三维人体配准分为原始深度图像的处理、对应点估计、点云配准过程。使用双边过滤对深度图像去噪,阈值法提取人体部分,应用向量场一致性算法进行对应点估计,并基于嵌入式形变模型构建基于法向量一致性正则项的配准模型。通过实验表明,本文方法加快了点云配准过程中的迭代速率,提高了点云配准精度,从而展现了本文配准方法的优势。

The key step of non-rigid human reconstruction is the non-rigid registration of 3D point clouds.This article focuses on the non-rigid registration method of point clouds.3D human registration is divided into processing of original depth images,corresponding point estimation,and point cloud registration.This article uses bilateral filtering to remove noise from the depth image,extracts the human part by the threshold method,applies the vector field consensus algorithm to estimate the corresponding point and builds a normal vector consistency regular terms based registration model based on the embedded deformation model.The results of test show this article’s method accelerates the iteration rate in point cloud registration and improves the accuracy of point cloud registration,so that demonstrates the advantages of this article s method of registration.