High resolution surface reconstruction from overlapping multiple-views

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

High resolution surface reconstruction from overlapping multiple-views

Full text

Pdf (293 KB)

Source

Annual Symposium on Computational Geometry archive
Proceedings of the 25th annual symposium on Computational geometry table of contents

Aarhus, Denmark

SESSION: Video and multimedia presentations table of contents

Pages 104-105

Year of Publication: 2009

ISBN:978-1-60558-501-7

Authors

Nader Salman	INRIA, Sophia Antipolis, France
Mariette Yvinec	INRIA, Sophia Antipolis, France

Sponsors

ACM: Association for Computing Machinery
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 17, Downloads (12 Months): 67, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1542362.1542386 What is a DOI?

ABSTRACT

Extracting a computer model of a real scene from a sequence of views, is one of the most challenging and fundamental problems in computer vision. Stereo vision algorithms allow us to extract from the images a sparse 3D point cloud on the scene surfaces. However, computing an accurate mesh of the scene based on such poor quality data points (noise, sparsity) is very difficult. Here we describe a simple yet original approach that uses both the stereo vision extracted point cloud and the calibrated images. Our method is a three-stage process in which the first stage merges, filters and smoothes the input 3D points. The second stage builds for each calibrated image a triangular depth-map and fuses the set of depth-maps into a triangle soup that minimize violations of size and visibility constraints. Finally, a mesh is computed from the triangle soup using a reconstruction method that combines restricted Delaunay triangulation and Delaunay refinement.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Jean-Daniel Boissonnat , Steve Oudot, Provably good sampling and meshing of surfaces, Graphical Models, v.67 n.5, p.405-451, September 2005 [doi>10.1016/j.gmod.2005.01.004]
	2	Elisabetta Bruzzone , M. Cazzanti , Leila De Floriani , Fulvia Mangili, Applying Two-dimensional Delaunay Triangulation to Stereo Data Interpolation, Proceedings of the Second European Conference on Computer Vision, p.368-372, May 19-22, 1992
	3	CGAL, Computational Geometry Algorithms Library. http://www.cgal.org.
	4	CIMG, C++ Template Image Processing Toolkit. http://cimg.sourceforge.net.
	5	O. D. Faugeras , E. Le Bras-Mehlman , J. D. Boissonnat, Representing stereo data with the Delaunay triangulation, Artificial Intelligence, v.44 n.1-2, p.41-87, July 1990 [doi>10.1016/0004-3702(90)90098-K]
	6	W. Frei , Chung-Ching Chen, Fast Boundary Detection: A Generalization and a New Algorithm, IEEE Transactions on Computers, v.26 n.10, p.988-998, October 1977 [doi>10.1109/TC.1977.1674733]
	7	A. Hilton. Scene modelling from sparse 3d data. Image and Vision Computing, 23(10):900 -- 920, 2005.
	8	P. Labatut, J.-P. Pons, and R. Keriven. Efficient multi-view reconstruction of large-scale scenes using interest points, delaunay triangulation and graph cuts. Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, pages 1--8, Oct. 2007.
	9	A. Laurentini, The Visual Hull Concept for Silhouette-Based Image Understanding, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.16 n.2, p.150-162, February 1994 [doi>10.1109/34.273735]
	10	Maxime Lhuillier , Long Quan, Surface Reconstruction by Integrating 3D and 2D Data of Multiple Views, Proceedings of the Ninth IEEE International Conference on Computer Vision, p.1313, October 13-16, 2003
	11	D. D. Morris and T. Kanade. Image-consistent surface triangulation. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, 1:1332, 2000.
	12	Multi-view evaluation. http://cvlab.epfl.ch/data.
	13	Jean-Philippe Pons , Renaud Keriven , Olivier Faugeras, Multi-View Stereo Reconstruction and Scene Flow Estimation with a Global Image-Based Matching Score, International Journal of Computer Vision, v.72 n.2, p.179-193, April 2007 [doi>10.1007/s11263-006-8671-5]
	14	L. Rineau and M. Yvinec. Meshing 3d domains bounded by piecewise smooth surfaces. In Meshing Roundtable conference proceedings, pages 443--460, 2007.
	15	Steven M. Seitz , Brian Curless , James Diebel , Daniel Scharstein , Richard Szeliski, A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p.519-528, June 17-22, 2006 [doi>10.1109/CVPR.2006.19]
	16	C. Strecha, W. von Hansen, L. Van Gool, P. Fua, and U. Thoennessen. On benchmarking camera calibration and multi-view stereo for high resolution imagery. Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1--8, June 2008.
	17	A. Treuille, A. Hertzmann, and S. Seitz. Example-based stereo with general BRDFs. In Proc. of the European Conference on Computer Vision, pages 457--469, 2004.