Consolidation of Unorganized Point Clouds for Surface Reconstruction

Hui Huang, Dan Li, Uri Ascher, Hao Zhang, Daniel Cohen-Or

Research output: Contribution to journalArticlepeer-review

Abstract

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, a weighted 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 wellconsolidated by our method steers conventional surface generation schemes towards a proper interpretation of the input data.

Original languageEnglish
Pages (from-to)1-7
Number of pages7
JournalACM Transactions on Graphics
Volume28
Issue number5
DOIs
StatePublished - 1 Dec 2009

Fingerprint

Dive into the research topics of 'Consolidation of Unorganized Point Clouds for Surface Reconstruction'. Together they form a unique fingerprint.

Cite this