Paper Info
Title
A novel rapid point-cloud surface reconstruction algorithm for laser imaging radar
Abstract
In order to obtain the fast three-dimensional surface reconstruction from given scattered point clouds, a novel improved point-cloud surface reconstruction algorithm for laser imaging radar is proposed so as to reconstruct the three-dimensional depth surface from the depth data and image data in this paper. Firstly, the three-dimensional space is partitioned into voxels with local distance points and finds outliers with point histogram features; then the Gaussian process (GP) regression is adopted to generate a plane similar to a Gaussian distribution; finally, the high-resolution gray data and three-dimensional interpolation points are fused by using Markov random fields to build a dense three-dimensional depth surface. Experimental results show that our proposed algorithm will greatly improve the robustness and reconstruction accuracy of three-dimensional surface reconstruction algorithm and can be used to assist unmanned driving in complex urban scenes.
Year
DOI
Venue
2019
10.1007/s11042-018-6244-6
Multimedia Tools and Applications
Keywords
Field
DocType
Automated vehicle operation, Laser rangefinder, Image data, Depth surface, Interpolation, Markov random field
Surface reconstruction,Computer vision,Histogram,Radar imaging,Computer science,Markov random field,Interpolation,Algorithm,Gaussian,Artificial intelligence,Gaussian process,Point cloud
Journal
Volume
Issue
ISSN
78.0
7
1573-7721
Citations 
PageRank 
References 
1
0.43
4
Authors
1
Name
Order
Citations
PageRank
Wendong Wang182172.69