Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Visual Computing

ISVC 2008: Advances in Visual Computing pp 897–904Cite as

3D Line Reconstruction of a Road Environment Using an In-Vehicle Camera

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5359))

Abstract

This paper describes a method for reconstructing 3D straight lines in the road environment from an image sequence captured by an in-vehicle camera. An actual road environment includes many straight lines such as edges of buildings, utility poles and road markings. These 3D lines can be used effectively for understanding the road scene; our proposed method, therefore, aims to reconstruct the 3D lines using an in-vehicle camera. The camera motion and the 3D line parameters are estimated simultaneously by minimizing the reprojection errors of corresponding edge segments in the image sequence. In the road environment, a forward-looking in-vehicle camera has difficultly in establishing a large parallax for accurate estimation of parameters. The accuracy in estimating parameters is improved by using constraints defined on the basis of a general knowledge of the structures. In experiments on an actual road environment, the camera motion and the 3D line parameters have been estimated from detected edge segments. The accuracy of the 3D lines was evaluated by comparing the estimated position and reference positions.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Zhao, H., Shibasaki, R.: A vehicle-borne urban 3-D acquisition system using single-row laser range scanners. IEEE Trans. SMC Part B: Cybernetics 33(4), 658–666 (2003); Proc. Int. Symp. on Mixed and Augmented Reality, pp. 110–119 (2004)

    Google Scholar 

  2. Asai, T., Kanbara, M., Yokoya, N.: 3D modeling of outdoor environments by integrating omnidirectional range and color images. In: Proc. Int. Conf. on 3-D Digital Imaging and Modeling, pp. 447–454 (2005)

    Google Scholar 

  3. Pollefeys, M., Koch, R., Vergauwen, M., Deknuydt, A.A., Van Gool, L.J.: Three-dimensional scene reconstruction from images. In: Proc. SPIE, vol. 3958, pp. 215–226 (2000)

    Google Scholar 

  4. Skrypnyk, I., Lowe, D.G.: Scene modelling, recognition and tracking with invariant image features. In: Proc. Int. Symp. on Mixed and Augmented Reality, pp. 110–119 (2004)

    Google Scholar 

  5. Okutomi, M., Kanade, T.: A Multiple-baseline stereo. IEEE Trans. On Pattern Analysis and Machine Intelligence 15(4), 353–363 (1993)

    Article  Google Scholar 

  6. Ikeda, S., Sato, T., Yamaguchi, K., Yokoya, N.: Construction of feature landmark database using omnidirectional videos and GPS positions. In: Proc. Int. Conf. on 3-D Digital Imaging and Modeling, pp. 249–256 (2007)

    Google Scholar 

  7. Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. Alvey Vision Conf., pp. 147–151 (1988)

    Google Scholar 

  8. Martinec, D., Pajdla, T.: Line Reconstruction from many perspective images by factorization. In: Proc. Int. Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 497–502 (2003)

    Google Scholar 

  9. Schindler, G., Krishnamurthy, P., Dellaert, F.: Line-based structure for motion from urban environments. In: Proc. Int. Symp. on 3D Data Processing, Visualization and Transmission, pp. 846–853 (2006)

    Google Scholar 

  10. Taylor, C.J., Kriegman, D.J.: Structure and motion from line segments in multiple images. IEEE Trans. on Pattern Analysis and Machine Intelligence 17(11), 1021–1033 (1995)

    Article  Google Scholar 

  11. Tang, A.W.K., Ng, T.P., Hung, Y.S., Leung, C.H.: Projective reconstruction from line-correspondences in multiple uncalibrated images. Pattern Recognition 39(5), 889–896 (2006)

    Article  MATH  Google Scholar 

  12. Lowe, D.G.: The viewpoint consistency constraint. Int. J. Computer Vision 1(1), 57–72 (1987)

    Article  MathSciNet  Google Scholar 

  13. Triggs, B., McLauchlan, P., Hartley, R., Fitzgibbon, A.: Bundle adjustment -A modern synthesis. Vision Algorithms: Theory and Practice, 298–375 (2000)

    Google Scholar 

  14. Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Toyota Central R&D Labs., Inc. Road Environment Recognision Lab., 41-1 Yokomichi, Nagakute, Aichi, Japan

    Toshihiro Asai, Koichiro Yamaguchi, Yoshiko Kojima, Takashi Naito & Yoshiki Ninomiya

Authors
  1. Toshihiro Asai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Koichiro Yamaguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yoshiko Kojima
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Takashi Naito
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yoshiki Ninomiya
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Mitsubishi Electric Research Laboratories, P.O. Box 02139, Cambridge, MA, USA

    Fatih Porikli

  7. Computer and Information Science and Engineering, University of Florida, P.O. Box, FL 32611-6120, Gainsville, USA

    Jörg Peters

  8. IBM T.J. Watson Research Center, 19 Skyline Drive, NY 10532, Hawthorne, USA

    James Klosowski

  9. 128 Memorial Mall, Stewart B001, IN 47907, West Lafayette, USA

    Laura Arns

  10. Denver Museum of Nature and Space, 2001 Colorade Blvd. Denver,, CO 80205, USA

    Yu Ka Chun

  11. Departmen of Computer Science,, NC State University, Campus Box 8206, NC 27695-8206, Raleigh, USA

    Theresa-Marie Rhyne

  12. Los Alamos National Labs, P.O. Box 1663, NM 87545, Los Alamos, USA

    Laura Monroe

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Asai, T., Yamaguchi, K., Kojima, Y., Naito, T., Ninomiya, Y. (2008). 3D Line Reconstruction of a Road Environment Using an In-Vehicle Camera. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89646-3_89

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-89646-3_89

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89645-6

  • Online ISBN: 978-3-540-89646-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation