Multi-View Stereo Point Clouds Visualization

  • Yi Gong
  • Yuan-Fang Wang
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Point Cloud Markov Random Field Dense Point Cloud View Interpolation Fast Approximate Energy Minimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yi Gong
    • 1
  • Yuan-Fang Wang
    • 1
  1. 1.Computer Science DepartmentUniversity of CaliforniaSanta BarbaraUSA

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