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3D Rheims reconstruction through ages: robust and invariant postcard matching

Authors:
Barbara Romaniuk
Reims University, France
,
Lara Younes
Reims University, France
,
Éric Bittar
Reims University, France
Authors Info & Claims
Published: 26 November 2012 Publication History

Abstract

We present the first step of the Rheims city (France) 3D reconstruction based on old postcards. Rheims endured drastic destruction during the First and then the Second World Wars. Old postcards, embodying the period from the beginning to the end of the 20th century, establish a testimony of the evolution in time and space of the most important places and monuments in the city. We aim to localize, render and visualize in 3D the buildings of this city, by using this sparse dataset and incomplete cadastral surveys. Image matching is initially required for locating a building in the city space. In this paper we propose a performance comparison of state-of-the-art detector-descriptor couples. We focus on their robustness and their transformations invariance when low resolution data is subject to temporal and space modifications.

References

[1]
S. Agarwal, Y. Furukawa, N. Snavely, B. Curless, S. M. Seitz, and R. Szeliski. Reconstructing Rome. Computer, 43: 40--47, 2010.
[2]
C. de Runz and E. Desjardin. Imperfect spatiotemporal information analysis in a GIS: Application to archaeological information completion hypothesis. In R. Jeansoulin, O. Papini, H. Prade, and S. Schockaert, editors, Methods for Handling Imperfect Spatial Information, volume 256 of Studies in Fuzziness and Soft Computing, pages 341--356. Springer Berlin, Heidelberg, 2010.
[3]
C. Gat, A. B. Albu, D. German, and E. Higgs. A comparative evaluation of feature detectors on historic repeat photography. In Proceedings of the 7th International Conference on Advances in Visual Computing - Volume Part II, ISVC'11, pages 701--714, 2011.
[4]
S. Gauglitz, T. Höllerer, and M. Turk. Evaluation of interest point detectors and feature descriptors for visual tracking. International Journal of Computer Vision, 94(3): 335--360, September 2011.
[5]
A. Gil, O. M. Mozos, M. Ballesta, and O. Reinoso. A comparative evaluation of interest point detectors and local descriptors for visual SLAM. Machine Vision Applications, 21(6): 905--920, October 2010.
[6]
Y. Ke and R. Sukthankar. Pca-sift: A more distinctive representation for local image descriptors. In Proceedings of the 17th IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'04, pages 506--513, 2004.
[7]
D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2): 91--110, November 2004.
[8]
B. Micusik and J. Kosecka. Piecewise planar city 3d modeling from street view panoramic sequences. In Proceedings of the 22nd IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'09, pages 2906--2912, 2009.
[9]
K. Mikolajczyk and C. Schmid. Scale and affine invariant interest point detectors. International Journal of Computer Vision, 60(1): 63--86, 2004.
[10]
K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis & Machine Intelligence, 27(10): 1615--1630, 2005.
[11]
P. Moreels and P. Perona. Evaluation of features detectors and descriptors based on 3D objects. International Journal of Computer Vision, 73(3): 263--284, July 2007.
[12]
J.-M. Morel and G. Yu. Asift: A new framework for fully affine invariant image comparison. SIAM J. Img. Sci., 2(2): 438--469, April 2009.
[13]
M. Pollefeys, R. Koch, M. Vergauwen, and L. Van Gool. Automated reconstruction of 3D scenes from sequences of images. ISPRS Journal of Photogrammetry and Remote Sensing, 55(4): 251--267, Nov. 2000.
[14]
N. Snavely, S. Seitz, and R. Szeliski. Modeling the world from internet photo collections. International Journal of Computer Vision, 80(2): 189--210, 2008.
[15]
C. Strecha, T. Pylvanainen, and P. Fua. Dynamic and scalable large scale image reconstruction. In Proceedings of the 23rd IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'19, pages 406--413, 2010.
[16]
L. Younes, B. Romaniuk, and E. Bittar. A comprehensive and comparative survey of the SIFT algorithm (feature detection, description, and characterization). In Proceedings of the 7th International Conference on Computer Vision Theory and Applications, VISAPP'12, pages 467--474, 2012.
[17]
G. Yu and J.-M. Morel. ASIFT: An Algorithm for Fully Affine Invariant Comparison. Image Processing On Line, 2011.

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cover image ACM Other conferences
IVCNZ '12: Proceedings of the 27th Conference on Image and Vision Computing New Zealand
November 2012
547 pages
ISBN:9781450314732
DOI:10.1145/2425836
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org

Sponsors

  • HRS: Hoare Research Software Ltd.
  • Google Inc.
  • Dept. of Information Science, Univ.of Otago: Department of Information Science, University of Otago, Dunedin, New Zealand

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 November 2012

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Author Tags

  1. Rheims
  2. image matching
  3. old postcards
  4. sparse data
  5. spatio-temporal 3D reconstruction

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Conference

IVCNZ '12
Sponsor:
  • HRS
  • Dept. of Information Science, Univ.of Otago
IVCNZ '12: Image and Vision Computing New Zealand
November 26 - 28, 2012
Dunedin, New Zealand

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Overall Acceptance Rate 55 of 74 submissions, 74%

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References

References

[1]
S. Agarwal, Y. Furukawa, N. Snavely, B. Curless, S. M. Seitz, and R. Szeliski. Reconstructing Rome. Computer, 43: 40--47, 2010.
[2]
C. de Runz and E. Desjardin. Imperfect spatiotemporal information analysis in a GIS: Application to archaeological information completion hypothesis. In R. Jeansoulin, O. Papini, H. Prade, and S. Schockaert, editors, Methods for Handling Imperfect Spatial Information, volume 256 of Studies in Fuzziness and Soft Computing, pages 341--356. Springer Berlin, Heidelberg, 2010.
[3]
C. Gat, A. B. Albu, D. German, and E. Higgs. A comparative evaluation of feature detectors on historic repeat photography. In Proceedings of the 7th International Conference on Advances in Visual Computing - Volume Part II, ISVC'11, pages 701--714, 2011.
[4]
S. Gauglitz, T. Höllerer, and M. Turk. Evaluation of interest point detectors and feature descriptors for visual tracking. International Journal of Computer Vision, 94(3): 335--360, September 2011.
[5]
A. Gil, O. M. Mozos, M. Ballesta, and O. Reinoso. A comparative evaluation of interest point detectors and local descriptors for visual SLAM. Machine Vision Applications, 21(6): 905--920, October 2010.
[6]
Y. Ke and R. Sukthankar. Pca-sift: A more distinctive representation for local image descriptors. In Proceedings of the 17th IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'04, pages 506--513, 2004.
[7]
D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2): 91--110, November 2004.
[8]
B. Micusik and J. Kosecka. Piecewise planar city 3d modeling from street view panoramic sequences. In Proceedings of the 22nd IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'09, pages 2906--2912, 2009.
[9]
K. Mikolajczyk and C. Schmid. Scale and affine invariant interest point detectors. International Journal of Computer Vision, 60(1): 63--86, 2004.
[10]
K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis & Machine Intelligence, 27(10): 1615--1630, 2005.
[11]
P. Moreels and P. Perona. Evaluation of features detectors and descriptors based on 3D objects. International Journal of Computer Vision, 73(3): 263--284, July 2007.
[12]
J.-M. Morel and G. Yu. Asift: A new framework for fully affine invariant image comparison. SIAM J. Img. Sci., 2(2): 438--469, April 2009.
[13]
M. Pollefeys, R. Koch, M. Vergauwen, and L. Van Gool. Automated reconstruction of 3D scenes from sequences of images. ISPRS Journal of Photogrammetry and Remote Sensing, 55(4): 251--267, Nov. 2000.
[14]
N. Snavely, S. Seitz, and R. Szeliski. Modeling the world from internet photo collections. International Journal of Computer Vision, 80(2): 189--210, 2008.
[15]
C. Strecha, T. Pylvanainen, and P. Fua. Dynamic and scalable large scale image reconstruction. In Proceedings of the 23rd IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'19, pages 406--413, 2010.
[16]
L. Younes, B. Romaniuk, and E. Bittar. A comprehensive and comparative survey of the SIFT algorithm (feature detection, description, and characterization). In Proceedings of the 7th International Conference on Computer Vision Theory and Applications, VISAPP'12, pages 467--474, 2012.
[17]
G. Yu and J.-M. Morel. ASIFT: An Algorithm for Fully Affine Invariant Comparison. Image Processing On Line, 2011.