Consolidation of Unorganized Point Clouds for Surface Reconstruction

ACM Transactions on Graphics
(Proceedings of SIGGRAPH ASIA 2009)

Hui Huang1          Dan Li1          Hao Zhang2          Uri Ascher1          Daniel Cohen-Or3
   1University of British Columbia          2Simon Fraser University          3Tel Aviv University      

Figure 1: Data consolidation, especially accurate normal estimation, from a noisy, unorganized, raw point cloud is crucial to obtaining a correct surface reconstruction. The right-most result is produced after applying our point cloud consolidation scheme.

We consolidate an unorganized point cloud with noise, outliers, non-uniformities, and in particular interference between close-by surface sheets as a preprocess to surface generation, focusing on reliable normal estimation.Our algorithm includes two new developments. First, aweighted locally optimal projection operator produces a set of denoised, outlier-free and evenly distributed particles over the original dense point cloud, so as to improve the reliability of local PCA for initial estimate of normals. Next, an iterative framework for robust normal estimation is introduced, where a priority-driven normal propagation scheme based on a new priority measure and an orientation-aware PCA work complementarily and iteratively to consolidate particle normals. The priority setting is reinforced with front stopping at thin surface features and normal flipping to enable robust handling of the close-by surface sheet problem.We demonstrate how a point cloud that is well consolidated by our method steers conventional surface generation schemes towards a proper interpretation of the input data.


Figure 2: Results gallery in the paper. 
  title = {Consolidation of unorganized point clouds for surface reconstruction},
  author = {Hui Huang and Dan Li and Hao Zhang and Uri Ascher and Daniel Cohen-Or},
  journal = {ACM Transactions on Graphics (Proc. SIGGRAPH Asia 2009)},
  volume = {28},
  issue = {5},
  pages = {176:1--176:7},
  year = {2009},

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