Consolidation of Unorganized Point Clouds for Surface Reconstruction
ACM Transactions on Graphics 2009
(Proceedings of SIGGRAPH ASIA 2009)

Hui Huang1*     Dan Li1     Hao Zhang2  Uri Ascher1  Daniel Cohen-Or3
   University of British Columbia    Simon Fraser University    Tel 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 = H. Huang and D. Li and H. Zhang and U. Ascher and D. Cohen-Or},
  journal = {ACM Transactions on Graphics},
  volume = {28},
  issue = {5},
  pages = {176:1--176:78},
  year = {2009}
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