TY - JOUR
T1 - A Large-Scale Evaluation Of Shape-Aware Neighborhood Weights And Neighborhood Sizes
AU - Skrodzki, Martin
AU - Zimmermann, Eric
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Classification
KW - Features
KW - Neighborhoods
KW - Point set
KW - Sigmoid
UR - http://www.scopus.com/inward/record.url?scp=85114838279&partnerID=8YFLogxK
U2 - 10.1016/j.cad.2021.103107
DO - 10.1016/j.cad.2021.103107
M3 - Article
AN - SCOPUS:85114838279
SN - 0010-4485
VL - 141
SP - 1
EP - 11
JO - CAD Computer Aided Design
JF - CAD Computer Aided Design
M1 - 103107
ER -