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Gradient Field Estimation on Triangle Meshes

Authors: MANCINELLI, CLAUDIO; Marco Livesu; Enrico Puppo;

Gradient Field Estimation on Triangle Meshes

Abstract

The estimation of the differential properties of a function sampled at the vertices of a discrete domain is at the basis of many applied sciences. In this paper, we focus on the computation of function gradients on triangle meshes. We study one face-based method (the standard the facto), plus three vertex based methods. Comparisons regard accuracy, ability to perform on different domain discretizations, and efficiency. We performed extensive tests and provide an in-depth analysis of our results. Besides some behaviour that is common to all methods, in our study we found that, considering both accuracy and efficiency, some methods are preferable to others. This directly translates to useful suggestions for the implementation of gradient estimators in research and industrial code.

CCS Concepts: Mathematics of computing --> Numerical differentiation; Computing methodologies --> Mesh models; Human-centered computing --> Scientific visualization

Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference

C. Mancinelli, M. Livesu, and E. Puppo

Geometry Processing Toolkits

87

96

Keywords

Mesh models, Scientific visualization, Numerical differentiation, centered computing, Mathematics of computing, Mathematics of computing: Numerical differentiation; Computing methodologies: Mesh models; Human-centered computing: Scientific visualization, Computing methodologies, Human

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
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