A Large-Scale Evaluation Of Shape-Aware Neighborhood Weights And Neighborhood Sizes

Martin Skrodzki*, Eric Zimmermann

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

In this paper, we define and evaluate a weighting scheme for neighborhoods in point sets. Our weighting takes the shape of the geometry, i.e., the normal information, into account. This causes the obtained neighborhoods to be more reliable in the sense that connectivity also depends on the orientation of the point set. We utilize a sigmoid to define the weights based on the normal variation. For an evaluation of the weighting scheme, we turn to a Shannon entropy model for feature classification that can be proven to be non-degenerate for our family of weights. Based on this model, we evaluate our weighting terms on a large scale of both clean and real-world models. This evaluation provides results regarding the choice of optimal parameters within our weighting scheme. Furthermore, the large-scale evaluation also reveals that neighborhood sizes should not be fixed globally when processing models. Finally, we highlight the applicability of our weighting scheme within the application context of denoising.

Original languageEnglish
Article number103107
Pages (from-to)1-11
Number of pages11
JournalCAD Computer Aided Design
Volume141
DOIs
Publication statusPublished - 2021

Keywords

  • Classification
  • Features
  • Neighborhoods
  • Point set
  • Sigmoid

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