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International Symposium on Visual Computing

ISVC 2011: Advances in Visual Computing pp 281–290Cite as

Multi-View Stereo Point Clouds Visualization

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6938))

Abstract

3D reconstruction from image sequences using multi-view stereo (MVS) algorithms is an important research area in computer vision and has multitude of applications. Due to its image-feature-based analysis, 3D point clouds derived from such algorithms are irregularly distributed and can be sparse at plain surface areas. Noise and outliers also degrade the resulting 3D clouds. Recovering an accurate surface description from such cloud data thus requires sophisticated post processing which can be computationally expensive even for small datasets. For time critical applications, plausible visualization is preferable. We present a fast and robust method for multi-view point splatting to visualize MVS point clouds. Elliptical surfels of adaptive sizes are used for better approximating the object surface, and view-independent textures are assigned to each surfel according to MRF-based energy optimization. The experiments show that our method can create surfel models with textures from low-quality MVS data within seconds. Rendering results are plausible with a small time cost due to our view-independent texture mapping strategy.

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Author information

Authors and Affiliations

  1. Computer Science Department, University of California, Santa Barbara, CA, 93106, USA

    Yi Gong & Yuan-Fang Wang

Authors
  1. Yi Gong
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  2. Yuan-Fang Wang
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Editor information

Editors and Affiliations

  1. University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, 89512, Reno, NV, USA

    Darko Koracin

  5. Department of Computer Science and Engineering, University of South Carolina, 29208, Columbia, SC, USA

    Song Wang

  6. HRL Laboratories, 3011 Malibu Canyon Road, 90265-4797, Malibu, CA, USA

    Kim Kyungnam

  7. Purdue University, 47907-2021, West Lafayette, IN, USA

    Bedrich Benes

  8. Sandia National Laboratory, 87185, Albuquerque, NM, USA

    Kenneth Moreland

  9. University of Louisiana, 70504, Lafayette, LA, USA

    Christoph Borst

  10. Adobe Systems Incorporated, San Francisco, CA, USA

    Stephen DiVerdi

  11. Polytechnic Institute of NYU, 11201, Brooklyn, NY, USA

    Chiang Yi-Jen

  12. Lawrence Livermore National Laboratory, 94551-0808, Livermore, CA, USA

    Jiang Ming

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© 2011 Springer-Verlag Berlin Heidelberg

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Gong, Y., Wang, YF. (2011). Multi-View Stereo Point Clouds Visualization. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24028-7_26

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  • DOI: https://doi.org/10.1007/978-3-642-24028-7_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24027-0

  • Online ISBN: 978-3-642-24028-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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