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GPU based techniques for deep image merging

Published:27 November 2017Publication History

ABSTRACT

Deep images store multiple per-pixel fragments, which include colour and depth information, compared to traditional 2D flat images which store only a single color value. Recently deep images are finding use in an increasing number of applications, including transparency and compositing. Compositing deep images requires merging per-pixel fragment lists in depth order, and little work has been presented on performant approaches.

This paper explores GPU based merging of deep images using different memory layouts for fragment lists --- linked lists, linearised arrays, and a new interleaved arrays approach. We also report performance improvements for merging techniques which leverage GPU memory hierarchy and layout by processing blocks of fragment data using fast registers based on similar techniques used to improve performance of transparency rendering. Our results show a 2--5X improvement from combining these techniques.

References

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  1. GPU based techniques for deep image merging

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      • Published in

        cover image ACM Conferences
        SA '17: SIGGRAPH Asia 2017 Technical Briefs
        November 2017
        108 pages
        ISBN:9781450354066
        DOI:10.1145/3145749

        Copyright © 2017 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 27 November 2017

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        Overall Acceptance Rate178of869submissions,20%

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