Volumetric Transparency with Per-Pixel Fragment Lists

Szécsi, László; Barta, Pál; Kovács, Balázs

doi:10.1201/b11642-26

Gpu Pro 3 2012

DOI: 10.1201/b11642-26

|View full text |Cite

Volumetric Transparency with Per-Pixel Fragment Lists

László Szécsi¹,

Pál Barta²,

Balázs Kovács³

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2015

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A variety of algorithms ranging from photorealistic rendering, such as global illumination [1], orderindependent transparency for forward, deferred, volumetric shading [2], [3], [4] and shadowing [5] to volume visualization and processing of flow, molecular, hair and solid geometry [6], [7], [8], [9], [10] require accurate multi-fragment processing at interactive speeds.…”

mentioning

confidence: 99%

<inline-formula><tex-math notation="LaTeX">$k^+$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="vasilakis-ieq1-2417581.gif"/></alternatives></inline-formula>-buffer: An Efficient, Memory-Friendly and Dynamic <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="vasilakis-ieq2-2417581.gif"/></alternatives></inline-formula>-buffer Fram

Vasilakis

Παπαϊωάννου

Fudos

2015

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Depth-sorted fragment determination is fundamental for a host of image-based techniques which simulates complex rendering effects. It is also a challenging task in terms of time and space required when rasterizing scenes with high depth complexity. When low graphics memory requirements are of utmost importance, k-buffer can objectively be considered as the most preferred framework which advantageously ensures the correct depth order on a subset of all generated fragments. Although various alternatives have been introduced to partially or completely alleviate the noticeable quality artifacts produced by the initial k-buffer algorithm in the expense of memory increase or performance downgrade, appropriate tools to automatically and dynamically compute the most suitable value of k are still missing. To this end, we introduce k(+)-buffer, a fast framework that accurately simulates the behavior of k-buffer in a single rendering pass. Two memory-bounded data structures: (i) the max-array and (ii) the max-heap are developed on the GPU to concurrently maintain the k-foremost fragments per pixel by exploring pixel synchronization and fragment culling. Memory-friendly strategies are further introduced to dynamically (a) lessen the wasteful memory allocation of individual pixels with low depth complexity frequencies, (b) minimize the allocated size of k-buffer according to different application goals and hardware limitations via a straightforward depth histogram analysis and (c) manage local GPU cache with a fixed-memory depth-sorting mechanism. Finally, an extensive experimental evaluation is provided demonstrating the advantages of our work over all prior k-buffer variants in terms of memory usage, performance cost and image quality.

show abstract

mentioning

confidence: 99%

<inline-formula><tex-math notation="LaTeX">$k^+$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="vasilakis-ieq1-2417581.gif"/></alternatives></inline-formula>-buffer: An Efficient, Memory-Friendly and Dynamic <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="vasilakis-ieq2-2417581.gif"/></alternatives></inline-formula>-buffer Fram

Vasilakis

Παπαϊωάννου

Fudos

2015

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Volumetric Transparency with Per-Pixel Fragment Lists

Cited by 1 publication

References 3 publications

Contact Info

Product

Resources

About