Article

Free access

Summed-area tables for texture mapping

Author:

Franklin C. CrowAuthors Info & Claims

SIGGRAPH '84: Proceedings of the 11th annual conference on Computer graphics and interactive techniques

Pages 207 - 212

https://doi.org/10.1145/800031.808600

Published: 01 January 1984 Publication History

PDF eReader

Abstract

Texture-map computations can be made tractable through use of precalculated tables which allow computational costs independent of the texture density. The first example of this technique, the “mip” map, uses a set of tables containing successively lower-resolution representations filtered down from the discrete texture function. An alternative method using a single table of values representing the integral over the texture function rather than the function itself may yield superior results at similar cost. The necessary algorithms to support the new technique are explained. Finally, the cost and performance of the new technique is compared to previous techniques.

References

[1]

Blinn. J. and Newell. M., "Texture and Reflection on Computer Generated Images", Communications of the ACM, Vol. 19, #10, Oct. 1976.

Digital Library

Google Scholar

[2]

Blinn. J., "Computer Display of Curved Surfaces", PhD. Dissertation, Department of Computer Science, University of Utah. December 1978.

Digital Library

Google Scholar

[3]

Catmull, E,. "A Subdivision Algorithm for Computer Display of Curved Surfaces", PhD. Dissertation, Department of Computer Science, University of Utah, Tech. Report UTEC-CSc-74-133, December 1974.

Digital Library

Google Scholar

[4]

4. Catmull. E. and Smith A. R., "3-D Transformation of Images in Scanline Order", Computer Graphics (Proc. Siggraph '80), Vol. 14. July 1980.

Digital Library

Google Scholar

[5]

Feibush, E. A., Levoy, M., and Cook, R. L., "Synthetic Texturing Using Digital Filters", Computer Graphics (Proc. Siggraph '80), Vol. 14, July 1980.

Digital Library

Google Scholar

[6]

Fournier, A., Fussell, D., and Carpenter, L., "Computer Rendering of Stochastic Models", Communications of the ACM, Vol. 25, #6, June 1982.

Digital Library

Google Scholar

[7]

Hackathorn. R. and Parent. R., Private Communication, 1980.

Google Scholar

[8]

Haruyama, S, and Barsky, B. A., "Using Stochastic Modeling for Texture Generation", IEEE Computer Graphics and Applications, Vol. 4, # 3, March 1984.

Digital Library

Google Scholar

[9]

Norton, A., Rockwood, A. P., and Skomolski, P. S., "Clamping: A Method of Antialiasing Textured Surfaces by Bandwidth Limiting in Object Space", Computer Graphics (Proc. Siggraph '82), Vol. 16, #3, July 1982.

Digital Library

Google Scholar

[10]

Williams, L. "Pyramidal Parametrics", Computer Graphics, Vol. 17, #3, July 1983.

Digital Library

Google Scholar

Cited By

View all

Nogami HKanetaka YNaganawa YMaeda YFukushima N(2024)Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance ImagesSensors10.3390/s2402063324:2(633)Online publication date: 19-Jan-2024
https://doi.org/10.3390/s24020633
Wu JJia MWu SXie S(2024)Fast Noise Level Estimation via the Similarity within and between PatchesElectronics10.3390/electronics1313255613:13(2556)Online publication date: 28-Jun-2024
https://doi.org/10.3390/electronics13132556
Cho HYoo H(2024)Enhancement of Three-Dimensional Computational Integral Imaging via Post-Processing with Visibility Coefficient EstimationApplied Sciences10.3390/app1413538414:13(5384)Online publication date: 21-Jun-2024
https://doi.org/10.3390/app14135384
Show More Cited By

Index Terms

Summed-area tables for texture mapping
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Texturing
    2. Rendering

Recommendations

Relief texture mapping
SIGGRAPH '00: Proceedings of the 27th annual conference on Computer graphics and interactive techniques

We present an extension to texture mapping that supports the representation of 3-D surface details and view motion parallax. The results are correct for viewpoints that are static or moving, far away or nearby. Our approach is very simple: a relief ...
Summed-area tables for texture mapping

Texture-map computations can be made tractable through use of precalculated tables which allow computational costs independent of the texture density. The first example of this technique, the “mip” map, uses a set of tables containing successively lower-...
Surface splatting
SIGGRAPH '01: Proceedings of the 28th annual conference on Computer graphics and interactive techniques

Modern laser range and optical scanners need rendering techniques that can handle millions of points with high resolution textures. This paper describes a point rendering and texture filtering technique called surface splatting which directly renders ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

SIGGRAPH '84: Proceedings of the 11th annual conference on Computer graphics and interactive techniques

January 1984

264 pages

ISBN:0897911385

DOI:10.1145/800031

ACM SIGGRAPH Computer Graphics Volume 18, Issue 3
July 1984
264 pages
ISSN:0097-8930
DOI:10.1145/964965
Issue’s Table of Contents

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 January 1984

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

SIGGRAPH '84

Sponsor:

SIGGRAPH '84: Computer graphics and interactive techniques

Acceptance Rates

SIGGRAPH '84 Paper Acceptance Rate 41 of 118 submissions, 35%;

Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1,036
Total Citations
View Citations
4,875
Total Downloads

Downloads (Last 12 months)391
Downloads (Last 6 weeks)54

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Nogami HKanetaka YNaganawa YMaeda YFukushima N(2024)Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance ImagesSensors10.3390/s2402063324:2(633)Online publication date: 19-Jan-2024
https://doi.org/10.3390/s24020633
Wu JJia MWu SXie S(2024)Fast Noise Level Estimation via the Similarity within and between PatchesElectronics10.3390/electronics1313255613:13(2556)Online publication date: 28-Jun-2024
https://doi.org/10.3390/electronics13132556
Cho HYoo H(2024)Enhancement of Three-Dimensional Computational Integral Imaging via Post-Processing with Visibility Coefficient EstimationApplied Sciences10.3390/app1413538414:13(5384)Online publication date: 21-Jun-2024
https://doi.org/10.3390/app14135384
Lu GGuo JKellnhofer PEisemann E(2024)Sheared Polygonal Texture FilteringProceedings of the 50th Graphics Interface Conference10.1145/3670947.3670952(1-7)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3670947.3670952
Rave HMolchanov VLinsen L(2024)Uniform Sample Distribution in Scatterplots via Sector-based Transformation2024 IEEE Visualization and Visual Analytics (VIS)10.1109/VIS55277.2024.00039(156-160)Online publication date: 13-Oct-2024
https://doi.org/10.1109/VIS55277.2024.00039
Pan LHartley RLiu LXu ZChowdhury SYang YZhang HLi HLiu M(2024)Weakly-Supervised Depth Estimation and Image Deblurring via Dual-Pixel SensorsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.345897446:12(11314-11330)Online publication date: Dec-2024
https://doi.org/10.1109/TPAMI.2024.3458974
Tang HLiu GQian YWang JXiong J(2024)EgeFusion: Towards Edge Gradient Enhancement in Infrared and Visible Image Fusion With Multi-Scale TransformIEEE Transactions on Computational Imaging10.1109/TCI.2024.336939810(385-398)Online publication date: 2024
https://doi.org/10.1109/TCI.2024.3369398
Sun QWang ZLeung CXiao Y(2024)Real-Time Lighting Effects for Consumer-Grade Mobile Graphics HardwareIEEE Transactions on Consumer Electronics10.1109/TCE.2023.332805170:1(338-349)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2023.3328051
Bethur NAgnesina ABrunion MGarcia-Ortiz ACatthoor FMilojevic DKomalan MCavalcante MRiedel SBenini LLim S(2024)Hier-3D: A Methodology for Physical Hierarchy Exploration of 3-D ICsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.334275343:7(1957-1970)Online publication date: Jul-2024
https://doi.org/10.1109/TCAD.2023.3342753
Yu ZChen AHuang BSattler TGeiger A(2024)Mip-Splatting: Alias-Free 3D Gaussian Splatting2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01839(19447-19456)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.01839
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Relief texture mapping

Summed-area tables for texture mapping

Surface splatting