Three-dimensional model restoration with shape-constrained sample filling

Guohua, 耿国华 Geng; Fan, 余 凡 Yu; Wen, 杨 稳 Yang; Xiao-ning, 刘晓宁 Liu

doi:10.3788/ope.20182611.2863

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The plane-guided tensor voting is proposed to restore the nonfeatured holes. Geng et al [9] proposed a way to repair the holes in the terracotta warriors, provided that the missing parts of the model are stored in a database. The boundary of the hole is identified based on which fragment model with a roughly equal area is found in the database.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Density Analysis of Repaired Point Cloud with Holes Based on Image Data

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Repairing point cloud holes has become an important problem in the research of 3D laser point cloud data, which ensures the integrity and improves the precision of point cloud data. However, for the point cloud data with non-characteristic holes, the boundary data of point cloud holes cannot be used for repairing. Therefore, this paper introduces photogrammetry technology and analyzes the density of the image point cloud data with the highest precision. The 3D laser point cloud data are first formed into hole data with sharp features. The image data are calculated into six density image point cloud data. Next, the barycenterization Bursa model is used to fine-register the two types of data and to delete the overlapping regions. Then, the cross-section is used to evaluate the precision of the combined point cloud data to get the optimal density. A three-dimensional model is constructed for this data and the original point cloud data, respectively and the surface area method and the deviation method are used to compare them. The experimental results show that the ratio of the areas is less than 0.5%, and the maximum standard deviation is 0.0036 m and the minimum is 0.0015 m.

show abstract

Section: Introductionmentioning

confidence: 99%