An Adaptive Point Sampler on a Regular Latice

Abdalla G.M. Ahmed; Till Niese; Hui Huang; Oliver Deussen

doi:10.1145/3072959.3073588

An Adaptive Point Sampler on a Regular Latice

Abdalla G.M. Ahmed, Till Niese, Hui Huang, Oliver Deussen

Research output: Contribution to conference › Paper › peer-review

23 Scopus citations

Abstract

We present a framework to distribute point samples with controlled spectral properties using a regular lattice of tiles with a single sample per tile. We employ a word-based identification scheme to identify individual tiles in the lattice. Our scheme is recursive, permitting tiles to be subdivided into smaller tiles that use the same set of IDs. The corresponding framework offers a very simple setup for optimization towards different spectral properties. Small lookup tables are suficient to store all the information needed to produce different point sets. For blue noise with varying densities, we employ the bit-reversal principle to recursively traverse sub-tiles. Our framework is also capable of delivering multi-class blue noise samples. It is well-suited for different sampling scenarios in rendering, including area-light sampling (uniform and adaptive), and importance sampling. Other applications include stippling and distributing objects.

Original language	English (US)
DOIs	https://doi.org/10.1145/3072959.3073588
State	Published - 2017
Event	ACM SIGGRAPH 2017 - Los Angeles, United States Duration: Jul 30 2017 → Aug 3 2017

Conference

Conference	ACM SIGGRAPH 2017
Country/Territory	United States
City	Los Angeles
Period	07/30/17 → 08/3/17

Bibliographical note

Funding Information:
We thank the anonymous reviewers for their detailed feedback to improve the paper. Thanks to Cengiz Öztireli for sharing the grid test scene. Thanks to Carla Avo-lio for the voice over of the supporting video clip. Corresponding author is Ab-dalla G. M. Ahmed, [email protected]. This work was partially funded by Deutsche Forschungsgemeinschaft Grant (DE-620/22-1), the National Foreign 1000 Talent Plan (WQ201344000169), Leading Talents of Guangdong Program (00201509), NSFC (61522213, 61379090, 61232011), Guangdong Science and Technology Program (2015A030312015), and Shenzhen Innovation Program (JCYJ20151015151249564). 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 the author(s) 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]. © 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM. 0730-0301/2017/7-ART138 $15.00 DOI: http://dx.doi.org/10.1145/3072959.3073588

Publisher Copyright:
© 2017 Copyright held by the owner/author(s).

Keywords

Blue noise
Monte Carlo
Multi-class blue noise
Quasi-Monte Carlo
Sampling
Self-similarity
Thue-Morse word
Tiling

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design

Access to Document

10.1145/3072959.3073588

Cite this

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title = "An Adaptive Point Sampler on a Regular Latice",

abstract = "We present a framework to distribute point samples with controlled spectral properties using a regular lattice of tiles with a single sample per tile. We employ a word-based identification scheme to identify individual tiles in the lattice. Our scheme is recursive, permitting tiles to be subdivided into smaller tiles that use the same set of IDs. The corresponding framework offers a very simple setup for optimization towards different spectral properties. Small lookup tables are suficient to store all the information needed to produce different point sets. For blue noise with varying densities, we employ the bit-reversal principle to recursively traverse sub-tiles. Our framework is also capable of delivering multi-class blue noise samples. It is well-suited for different sampling scenarios in rendering, including area-light sampling (uniform and adaptive), and importance sampling. Other applications include stippling and distributing objects.",

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note = "Funding Information: We thank the anonymous reviewers for their detailed feedback to improve the paper. Thanks to Cengiz {\"O}ztireli for sharing the grid test scene. Thanks to Carla Avo-lio for the voice over of the supporting video clip. Corresponding author is Ab-dalla G. M. Ahmed, [email protected]. This work was partially funded by Deutsche Forschungsgemeinschaft Grant (DE-620/22-1), the National Foreign 1000 Talent Plan (WQ201344000169), Leading Talents of Guangdong Program (00201509), NSFC (61522213, 61379090, 61232011), Guangdong Science and Technology Program (2015A030312015), and Shenzhen Innovation Program (JCYJ20151015151249564). 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 the author(s) 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]. {\textcopyright} 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM. 0730-0301/2017/7-ART138 $15.00 DOI: http://dx.doi.org/10.1145/3072959.3073588 Publisher Copyright: {\textcopyright} 2017 Copyright held by the owner/author(s).; ACM SIGGRAPH 2017 ; Conference date: 30-07-2017 Through 03-08-2017",

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N2 - We present a framework to distribute point samples with controlled spectral properties using a regular lattice of tiles with a single sample per tile. We employ a word-based identification scheme to identify individual tiles in the lattice. Our scheme is recursive, permitting tiles to be subdivided into smaller tiles that use the same set of IDs. The corresponding framework offers a very simple setup for optimization towards different spectral properties. Small lookup tables are suficient to store all the information needed to produce different point sets. For blue noise with varying densities, we employ the bit-reversal principle to recursively traverse sub-tiles. Our framework is also capable of delivering multi-class blue noise samples. It is well-suited for different sampling scenarios in rendering, including area-light sampling (uniform and adaptive), and importance sampling. Other applications include stippling and distributing objects.

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KW - Quasi-Monte Carlo

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KW - Self-similarity

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ER -

An Adaptive Point Sampler on a Regular Latice

Abstract

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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