Selectively densified 3D object modeling based on regions of interest detection using neural gas networks-Reference-Cited by-同舟云学术

Selectively densified 3D object modeling based on regions of interest detection using neural gas networks

Published:2016-03-31 Issue:18 Volume:21 Page:5443-5455
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Cretu Ana-Maria,Chagnon-Forget Maude,Payeur Pierre

Funder

Natural Sciences and Engineering Research Council of Canada (CA)

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

http://link.springer.com/article/10.1007/s00500-016-2132-z/fulltext.html

Reference37 articles.

1. A Benchmark for 3D Interest Points Marked by Human Subjects [Online]. http://www.itl.nist.gov/iad/vug/sharp/benchmark/3DInterestPoint/

2. Castellani U, Cristiani M, Fantoni S, Murino V (2008) Sparse points matching by combining 3D mesh saliency with statistical descriptors. Eurographics 27(2):643–652

3. Cignoni P, Rocchini C, Scopigno R (1998) Metro: measuring error on simplified surfaces. Comput Graph Forum 17(2):167–174. [AAOnline]. http://vcg.isti.cnr.it/activities/surfacegrevis/simplification/

4. CloudCompare—3D Point Cloud and Mesh Processing Software, Open Source Project [Online]. http://www.danielgm.net/cc/

5. Cretu A-M (2009) Experimental data acquisition and modeling of 3D deformable objects. Ph.D. Thesis, University of Ottawa

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