重庆工商大学学报（自然科学版）2025，Vol.42Issue(4)：53-61,9.DOI:10.16055/j.issn.1672-058X.2025.0004.007
基于多视角的面颈部点云配准算法研究
Research on Face and Neck Point Cloud Registration Algorithm Based on Multi-view Perspective
摘要
Abstract
Objective To address the deformation issues in non-rigid registration algorithms for the three-dimensional reconstruction of facial and neck regions,this paper designed a method for collecting point clouds of the facial and neck regions and proposed an adaptive local registration algorithm that restores global registration.Methods A depth camera was used to collect point clouds at three positions of the head:the right mandibular angle,the left mandibular angle,and the nasal bridge.The collected point clouds were preprocessed and the RGB information of the point cloud was converted into HSV(Hue,Saturation,Value).The position of the lips was located,and the facial area was segmented to retain the nose and mouth regions.The random sample consensus(RANSAC)algorithm combined with the three-dimensional shape context(3DSC)feature was applied for coarse registration,followed by the iterative closest point(ICP)algorithm for fine registration.Finally,the transformation matrix obtained from local registration was applied to the original point cloud to obtain a three-dimensional point cloud model of the facial and neck regions.Results Experimental results showed that the point clouds collected at the three positions could fully cover the entire facial and neck region.By comparing five improved iterative closest point registration algorithms,it was found that the registration accuracy was the highest when using the 3DSC+RANSAC+ICP algorithm.By setting facial and neck landmarks and conducting registration experiments with different facial data,the registration results were compared with the distances between the marked points on the real face,with errors all less than 2.5 mm,verifying the registration accuracy and robustness of the algorithm.Conclusion The designed multi-view facial and neck registration algorithm can effectively register,with registration errors less than 2.5 mm compared with real faces,addressing the deformation issues in non-rigid registration algorithms for three-dimensional reconstruction of facial and neck regions and demonstrating certain robustness when processing facial and neck data from different individuals.关键词
多视角点云/刚性配准/三维重建/面颈部Key words
multi-view point cloud/rigid registration/three-dimensional reconstruction/facial and neck regions分类
信息技术与安全科学引用本文复制引用
黄天齐,严楠,戴家树,李萌阳..基于多视角的面颈部点云配准算法研究[J].重庆工商大学学报（自然科学版）,2025,42(4):53-61,9.基金项目
安徽未来技术研究院企业合作项目(2023QYHZ11). （2023QYHZ11）
