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Procedural noise using sparse Gabor convolution

Authors:

Sylvain Lefebvre,

George Drettakis,

Philip DutréAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 28, Issue 3

Article No.: 54, Pages 1 - 10

https://doi.org/10.1145/1531326.1531360

Published: 27 July 2009 Publication History

Abstract

Noise is an essential tool for texturing and modeling. Designing interesting textures with noise calls for accurate spectral control, since noise is best described in terms of spectral content. Texturing requires that noise can be easily mapped to a surface, while high-quality rendering requires anisotropic filtering. A noise function that is procedural and fast to evaluate offers several additional advantages. Unfortunately, no existing noise combines all of these properties.

In this paper we introduce a noise based on sparse convolution and the Gabor kernel that enables all of these properties. Our noise offers accurate spectral control with intuitive parameters such as orientation, principal frequency and bandwidth. Our noise supports two-dimensional and solid noise, but we also introduce setup-free surface noise. This is a method for mapping noise onto a surface, complementary to solid noise, that maintains the appearance of the noise pattern along the object and does not require a texture parameterization. Our approach requires only a few bytes of storage, does not use discretely sampled data, and is nonperiodic. It supports anisotropy and anisotropic filtering. We demonstrate our noise using an interactive tool for noise design.

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Index Terms

Procedural noise using sparse Gabor convolution
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Texturing

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 28, Issue 3

August 2009

750 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1531326

Issue’s Table of Contents

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 July 2009

Published in TOG Volume 28, Issue 3

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Cited By

Wang XWang FHong YBan X(2024)Transferability in Data Poisoning Attacks on Spatiotemporal Traffic Forecasting ModelsSSRN Electronic Journal10.2139/ssrn.4827065Online publication date: 2024
https://doi.org/10.2139/ssrn.4827065
Xiao ZJiang HDeng ZLi RHan WWang Z(2024)Large Scale Farm Scene Modeling from Remote Sensing ImageryACM Transactions on Graphics10.1145/368791843:6(1-12)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687918
Fernandes LCetinaslan OCoelho A(2024)Enhancing Mesh Deformation Realism for Synthesizing WrinklesSIGGRAPH Asia 2024 Technical Communications10.1145/3681758.3698011(1-4)Online publication date: 3-Dec-2024
https://dl.acm.org/doi/10.1145/3681758.3698011
Maesumi AHu DSaripalli KKim VFisher MPirk SRitchie D(2024)One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise PatternsACM Transactions on Graphics10.1145/365819543:4(1-21)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3658195
Tariq TDidyk P(2024)Towards Motion Metamers for Foveated RenderingACM Transactions on Graphics10.1145/365814143:4(1-10)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3658141
Chermain XZanni CMartínez JHugron PLefebvre S(2023)Orientable Dense Cyclic Infill for Anisotropic Appearance FabricationACM Transactions on Graphics10.1145/359241242:4(1-13)Online publication date: 26-Jul-2023
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Grenier CGuérin ÉGalin ÉSauvage B(2023)Real‐time Terrain Enhancement with Controlled Procedural PatternsComputer Graphics Forum10.1111/cgf.1499243:1Online publication date: 23-Nov-2023
https://doi.org/10.1111/cgf.14992
Grenier CSauvage BDischler JThery S(2023)Color‐mapped noise vector fields for generating procedural micro‐patternsComputer Graphics Forum10.1111/cgf.1469341:7(477-487)Online publication date: 20-Mar-2023
https://doi.org/10.1111/cgf.14693
Yan JYin HGe WLiu L(2023)Exploring aesthetic procedural noise for crafting model-agnostic universal adversarial perturbationsDisplays10.1016/j.displa.2023.10247979(102479)Online publication date: Sep-2023
https://doi.org/10.1016/j.displa.2023.102479
Yan MWang JShen YLv C(2022)A non-photorealistic rendering method based on Chinese ink and wash painting style for 3D mountain modelsHeritage Science10.1186/s40494-022-00825-z10:1Online publication date: 17-Nov-2022
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