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Conference Papers Year : 2023

Feature-Sized Sampling for Vector Line Art

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 ε-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 ε-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 ε 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.
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Dates and versions

hal-04224967 , version 1 (02-10-2023)

Identifiers

  • HAL Id : hal-04224967 , version 1

Cite

Stefan Ohrhallinger, Amal Dev Parakkat, Pooran Memari. Feature-Sized Sampling for Vector Line Art. Pacific Graphics 2023 - The 31th Pacific Conference on Computer Graphics and Applications, Oct 2023, Daejeon, South Korea. ⟨hal-04224967⟩
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