Automatic least-squares projection of points onto point clouds with applications in reverse engineering

  • Authors:
  • Yu-Shen Liu;Jean-Claude Paul;Jun-Hai Yong;Pi-Qiang Yu;Hui Zhang;Jia-Guang Sun;Karthik Ramani

  • Affiliations:
  • School of Software, Tsinghua University, Beijing 100084, PR China and Purdue Research and Education Center for Information Systems in Engineering (PRECISE), Purdue University, West Lafayette, IN 4 ...;School of Software, Tsinghua University, Beijing 100084, PR China;School of Software, Tsinghua University, Beijing 100084, PR China;School of Computer and Information Technology, Beijing Jiaotong University, PR China;School of Software, Tsinghua University, Beijing 100084, PR China;School of Software, Tsinghua University, Beijing 100084, PR China;Purdue Research and Education Center for Information Systems in Engineering (PRECISE), Purdue University, West Lafayette, IN 47907-2024, USA

  • Venue:
  • Computer-Aided Design
  • Year:
  • 2006

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Abstract

A novel method for projecting points onto a point cloud, possibly with noise, is presented based on the point directed projection (DP) algorithm proposed by Azariadis P., Sapidis N. [Drawing curves onto a cloud of points for point-based modelling. Computer-Aided Design 2005; 37(1): 109-22]. The new method operates directly on the point cloud without any explicit or implicit surface reconstruction procedure. The presented method uses a simple, robust, and efficient algorithm: least-squares projection (LSP), which projects points onto the point cloud in a least-squares sense without any specification of the projection vector. The main contribution of this novel method is the automatic computation of the projection vector. Furthermore, we demonstrate the effectiveness of this approach through a number of application examples including thinning a point cloud, point normal estimation, projecting curves onto a point cloud and others.