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Dense 3-D Reconstruction of an Outdoor Scene by Hundreds-Baseline Stereo Using a Hand-Held Video Camera

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Abstract

Three-dimensional (3-D) models of outdoor scenes are widely used for object recognition, navigation, mixed reality, and so on. Because such models are often made manually with high costs, automatic 3-D reconstruction has been widely investigated. In related work, a dense 3-D model is generated by using a stereo method. However, such approaches cannot use several hundreds images together for dense depth estimation because it is difficult to accurately calibrate a large number of cameras. In this paper, we propose a dense 3-D reconstruction method that first estimates extrinsic camera parameters of a hand-held video camera, and then reconstructs a dense 3-D model of a scene. In the first process, extrinsic camera parameters are estimated by tracking a small number of predefined markers of known 3-D positions and natural features automatically. Then, several hundreds dense depth maps obtained by multi-baseline stereo are combined together in a voxel space.So, we can acquire a dense 3-D model of the outdoor scene accurately by using several hundreds input images captured by a hand-held video camera.

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References

  • Barnard, S.T. and Fischler, M.A. 1982. Computational stereo. ACM Computing Surveys, 14(4):553–572.

    Google Scholar 

  • Beardsley, P., Zisserman, A., and Murray, D. 1997. Sequential updating of projective and affine structure from motion. Int. Jour. of Computer Vision, 23(3):235–259.

    Google Scholar 

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

  • Kumar, R., Sawhney, H.S., Guo, Y., Hsu, S., and Samarasekera, S. 2000. 3D manipulation of motion imagery. In Proc. Int. Conf. on Image Processing, pp. 17–20.

  • Morris, D.D. and Kanade, T. 1998. A unified factorization algorithm for points, lines segments and planes with uncertainty models. In Proc. 6th Int. Conf. on Computer Vision, pp. 696–702.

  • Ohta, Y. and Kanade, T. 1985. Stereo by intra-and inter-scanline search using dynamic programming. IEEE Trans. Pattern Analysis and Machine Intelligence, PAMI-7(2):139–154.

    Google Scholar 

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

    Google Scholar 

  • Poleman, J. and Kanade, T. 1993. A paraperspective factorization method for shape and motion recovery. Technical Report CMU-CS-93-219, Carnegie-Mellon University.

  • Pollefeys, M., Koch, R., Vergauwen, M., Deknuydt, A.A., and Gool, L.J.V. 2000. Three-dimentional scene reconstruction from images. In Proc. SPIE, vol. 3958, pp. 215–226.

    Google Scholar 

  • Roth, G. and Whitehead, A. 2000. Using projective vision to find camera positions in an image sequence. In Proc. 13th Int. Conf. on Vision Interface, pp. 87–94.

  • Sato, T., Kanbara, M., Takemura, H., and Yokoya, N. 2001. 3-D reconstruction from a monocular image sequence by tracking markers and natural features. In Proc. 14th Int. Conf. on Vision Interface, pp. 157–164.

  • Sawhney, H.S., Guo, Y., Asmuth, J., and Kumar, R. 1999. Multi-view 3D estimation and application to match move. In Proc. IEEE Workshop on Multi-view Modeling and Analysis of Visual Scenes, pp. 21–28.

  • Schmid, C., Mohr, R., and Bauckhage, C. 1998. Comparing and evaluating interest points. In Proc. 6th Int. Conf. on Computer Vision, pp. 230–235.

  • Tomasi, C. and Kanade, T. 1992. Shape and motion from image streams under orthography: A factorization method. Int. Journal of Computer Vision, 9(2):137–154.

    Google Scholar 

  • Tsai, R.Y. 1986. An efficient and accurate camera calibration technique for 3D machine vision. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 364–374.

  • Yokoya, N. 1992. Surface reconstruction directly from binocular stereo images by multiscale-multistage regularization. In Proc. 11th Int. Conf. on Pattern Recognition, vol. I, pp. 642–646.

    Google Scholar 

  • Yokoya, N., Shakunaga, T., and Kanbara, M. 1999. Passive range sensing techniques: Depth from images. IEICE Trans. Inf. and Syst., E82-D(3):523–533.

    Google Scholar 

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Authors and Affiliations

  1. Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0101, Japan

    Tomokazu Sato, Masayuki Kanbara & Naokazu Yokoya

  2. Cybermedia Center, Osaka University, 1-30 Machikaneyama, Toyonaka, Osaka, 560-0048, Japan

    Haruo Takemura

Authors
  1. Tomokazu Sato
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  2. Masayuki Kanbara
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  3. Naokazu Yokoya
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  4. Haruo Takemura
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Sato, T., Kanbara, M., Yokoya, N. et al. Dense 3-D Reconstruction of an Outdoor Scene by Hundreds-Baseline Stereo Using a Hand-Held Video Camera. International Journal of Computer Vision 47, 119–129 (2002). https://doi.org/10.1023/A:1014537706773

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  • DOI: https://doi.org/10.1023/A:1014537706773

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