A Large-Scale Evaluation Of Shape-Aware Neighborhood Weights And Neighborhood Sizes. (December 2021) - Skrodzki, Martin; Zimmermann, Eric
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. Graphical abstract: Highlights: Definition of a shape-aware neighborhood weighting utilizing sigmoid function weights based on normal variation. Presentation of a Shannon entropy evaluation model that can be proven to be non-degenerate on our inputs. Large scale experimental evaluation of the proposed neighborhood weighting concept. Discussion of the results with respect to both neighborhood weighting and neighborhood sizes.