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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| Published in | ACM transactions on graphics Vol. 32; no. 1; pp. 1 - 12 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
Association for Computing Machinery
01.01.2013
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| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Wu, Shihao Cohen-Or, Daniel Gong, Minglun Zhang, Hao (Richard) Ascher, Uri Huang, Hui |
| Author_xml | – sequence: 1 givenname: Hui surname: Huang fullname: Huang, Hui organization: Shenzhen Key Lab of Visual Computing and Visual Analytics/SIAT – sequence: 2 givenname: Shihao surname: Wu fullname: Wu, Shihao organization: South China University of Technology, China – sequence: 3 givenname: Minglun surname: Gong fullname: Gong, Minglun organization: Memorial University of Newfoundland – sequence: 4 givenname: Daniel surname: Cohen-Or fullname: Cohen-Or, Daniel organization: Tel-Aviv University, Israel – sequence: 5 givenname: Uri surname: Ascher fullname: Ascher, Uri organization: University of British Columbia – sequence: 6 givenname: Hao (Richard) surname: Zhang fullname: Zhang, Hao (Richard) organization: Simon Fraser University |
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| Keywords | 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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| Snippet | Points acquired by laser scanners are not intrinsically equipped with normals, which are essential to surface reconstruction and point set rendering using... |
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| SubjectTerms | 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 |
| Title | Edge-aware point set resampling |
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| Volume | 32 |
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