Edge-aware point set resampling

Points acquired by laser scanners are not intrinsically equipped with normals, which are essential to surface reconstruction and point set rendering using surfels. Normal estimation is notoriously sensitive to noise. Near sharp features, the computation of noise-free normals becomes even more challe...

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Bibliographic Details
Published inACM transactions on graphics Vol. 32; no. 1; pp. 1 - 12
Main Authors Huang, Hui, Wu, Shihao, Gong, Minglun, Cohen-Or, Daniel, Ascher, Uri, Zhang, Hao (Richard)
Format Journal Article
LanguageEnglish
Published New York, NY Association for Computing Machinery 01.01.2013
Subjects
Applied sciences
Artificial intelligence
Computation
Computer science; control theory; systems
Exact sciences and technology
Information retrieval. Graph
Pattern recognition. Digital image processing. Computational geometry
Theoretical computing
upsampling
Scanner
Singularity
Point set resampling
Bilateral
Modeling
normal estimation
Subsampling
Outlier
Surface reconstruction
Consolidation
Algorithms
Smoothing
Spanning tree
Remolded sample
Computer graphics
Laser
Ear
edge aware
surtel point set rendenng
point set
Sampling
Edge detection
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Summary:Points acquired by laser scanners are not intrinsically equipped with normals, which are essential to surface reconstruction and point set rendering using surfels. Normal estimation is notoriously sensitive to noise. Near sharp features, the computation of noise-free normals becomes even more challenging due to the inherent undersampling problem at edge singularities. As a result, common edge-aware consolidation techniques such as bilateral smoothing may still produce erroneous normals near the edges. We propose a resampling approach to process a noisy and possibly outlier-ridden point set in an edge-aware manner. Our key idea is to first resample away from the edges so that reliable normals can be computed at the samples, and then based on reliable data, we progressively resample the point set while approaching the edge singularities. We demonstrate that our Edge-Aware Resampling (EAR) algorithm is capable of producing consolidated point sets with noise-free normals and clean preservation of sharp features. We also show that EAR leads to improved performance of edge-aware reconstruction methods and point set rendering techniques.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:0730-0301
1557-7368
DOI:10.1145/2421636.2421645