A New Simplification Algorithm for Scattered Point Clouds with Feature Preservation

Miao Gong,Dan Zeng,Zhijiang Zhang

doi:10.3390/sym13030399

Abstract

High-precision and high-density three-dimensional point cloud models usually contain redundant data, which implies extra time and hardware costs in the subsequent data processing stage. To analyze and extract data more effectively, the point cloud must be simplified before data processing. Given that point cloud simplification must be sensitive to features to ensure that more valid information can be saved, in this paper, a new simplification algorithm for scattered point clouds with feature preservation, which can reduce the amount of data while retaining the features of data, is proposed. First, the Delaunay neighborhood of the point cloud is constructed, and then the edge points of the point cloud are extracted by the edge distribution characteristics of the point cloud. Second, the moving least-square method is used to obtain the normal vector of the point cloud and the valley ridge points of the model. Then, potential feature points are identified further and retained on the basis of the discrete gradient idea. Finally, non-feature points are extracted. Experimental results show that our method can be applied to models with different curvatures and effectively avoid the hole phenomenon in the simplification process. To further improve the robustness and anti-noise ability of the method, the neighborhood of the point cloud can be extended to multiple levels, and a balance between simplification speed and accuracy needs to be found.

Highlights

Three-dimensional (3D) scanning technology has always been the focus of research on computer vision, reverse engineering, and computer graphics [1]
This study focuses on the scattered data point cloud, which is distributed irregularly and unordered
This method is named as the detail feature points simplified algorithm (DFPSA) for a 3D point cloud

Summary

Introduction

Three-dimensional (3D) scanning technology has always been the focus of research on computer vision, reverse engineering, and computer graphics [1]. Ji et al [18] proposed a new K-neighborhood search method based on distance and density, and feature points are selected according to their geometric features. This method is named as the detail feature points simplified algorithm (DFPSA) for a 3D point cloud. Starting from what is already known in this field, a new simplification algorithm for scattered point clouds with feature preservation is proposed, so that it can simplify the point cloud and save as much information as possible. Hk-onweaerevsetrn, etihgehbsaomrs polfifnegatdureenpsiotiynts may be of many point cloluocdatmedodinelosnveasridiees,oaf nthde tahreeak. -Tnheuasr,etshtenDeieglahubnoarys noefifgehabtourrheopoodi[n2t3s]misaaydobpeted in this lstpootrucotadjhetyeec,dtptihoilnanantopeinso,teionthstoieqdbpstilineototrtuaoocFotdijhainefoygelc,tbutpphptitrholrealeaaanoin2tnanjpe,eiresctoteh,hittiasotinee.hoftofieTniDqtbrhlitsetopettaulcod-oaiosaniuo,lrtndobnpptthteralpaaroqeyionjnDie.DeincnetTeitheltsiahileogpaufienDhuintntipbenae,nlyodoaawtiryunthnheonetenoiappqgesoyiohiep.dginbrhnThfeo(teohNibrgprrehoeihDpmnoreibn,lonahouwdeDnotraihhyeoogee)ofdlhpaoospbeupd[froi2hnsfp(r3oeaNroh]rreyiemopDcinsoonettlrrDdeaadpiuidaegnea.nloahdnaAygpbud)asuotnoseprslafhaNdhrypotoop1ioiotoDjwnriednneicalnttaathnuoenpingidnn.dsauyApl.asrtosiojhenocwtoednn

The Edge Distribution Characteristics

Determination of Valley-Ridge Feature Points

The Self-Adapting Experiment Parameters

Comparison of the Simplification Effect with Other Methods

Method

Conclusions