DocumentCode
3167420
Title
Out-of-Core Surface Reconstruction from Large Point Sets for Infrastructure Inspection
Author
Chen Xu ; Frechet, Simon ; Laurendeau, Denis ; Miralles, Francois
Author_Institution
CSVL, Laval Univ., Quebec City, QC, Canada
fYear
2015
fDate
3-5 June 2015
Firstpage
313
Lastpage
319
Abstract
This paper presents a simple, effective, yet fast out-of-core method for surface reconstruction based on the vector field surface representation. The algorithm is designed to handle massive amount of real-world point clouds representing large infrastructures (e.g. underwater hydroelectric structure) acquired by LiDAR, sonar or laser scanning system using out-of-core techniques. Our method allows performing seamless surface reconstruction from unorganized, unrented, non-uniform and highly noisy data that include outliers. The applicability of the method has been evaluated in the context of hydroelectric infrastructure inspection, and its performance has been tested using synthetically produced data and field data captured on different Hydro-Quebec´s sites by laser line scanning, LiDAR and sonar measurement systems.
Keywords
computational geometry; computer graphics; image processing; inspection; optical radar; optical scanners; sonar; structural engineering computing; Hydro-Quebec sites; LiDAR; Voronoi diagram; computer graphics; hydroelectric infrastructure inspection; image processing; infrastructure inspection; large point sets; laser line scanning; laser scanning system; noisy data; out-of-core surface reconstruction; out-of-core techniques; real-world point clouds; sonar measurement systems; underwater hydroelectric structure; vector field surface representation; Image reconstruction; Laser radar; Sonar; Surface emitting lasers; Surface reconstruction; Surface treatment; Three-dimensional displays; Surface reconstruction; large point cloud; out-of-core processing; vector field;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Robot Vision (CRV), 2015 12th Conference on
Conference_Location
Halifax, NS
Type
conf
DOI
10.1109/CRV.2015.48
Filename
7158935
Link To Document