Feature-Sized Sampling for Vector Line Art
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Date
2023
Authors
Ohrhallinger, Stefan
Parakkat, Amal Dev
Memari, Pooran
Parakkat, Amal Dev
Memari, Pooran
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
By introducing a first-of-its-kind quantifiable sampling algorithm based on feature size, we present a fresh perspective on the practical aspects of planar curve sampling. Following the footsteps of e-sampling, which was originally proposed in the context of curve reconstruction to offer provable topological guarantees [ABE98] under quantifiable bounds, we propose an arbitrarily precise e-sampling algorithm for sampling smooth planar curves (with a prior bound on the minimum feature size of the curve). This paper not only introduces the first such algorithm which provides user-control and quantifiable precision but also highlights the importance of such a sampling process under two key contexts: 1) To conduct a first study comparing theoretical sampling conditions with practical sampling requirements for reconstruction guarantees that can further be used for analysing the upper bounds of e for various reconstruction algorithms with or without proofs, 2) As a feature-aware sampling of vector line art that can be used for applications such as coloring and meshing.
Description
CCS Concepts: Computing methodologies -> Point-based models; Parametric curve and surface models
@inproceedings{10.2312:pg.20231268,
booktitle = {Pacific Graphics Short Papers and Posters},
editor = {Chaine, Raphaëlle and Deng, Zhigang and Kim, Min H.},
title = {{Feature-Sized Sampling for Vector Line Art}},
author = {Ohrhallinger, Stefan and Parakkat, Amal Dev and Memari, Pooran},
year = {2023},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-234-9},
DOI = {10.2312/pg.20231268}
}