Data Parallel Ray Tracing of Massive Scenes based on Neural Proxy
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Date
2024
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Data-parallel ray tracing is an important method for rendering massive scenes that exceed local memory. Nevertheless, its efficacy is markedly contingent upon bandwidth owing to the substantial ray data transfer during the rendering process. In this paper, we advance the utilization of neural representation geometries in data-parallel rendering to reduce ray forwarding and intersection overheads. To this end, we introduce a lightweight geometric neural representation, denoted as a ''neural proxy.'' Utilizing our neural proxies, we propose an efficient data-parallel ray tracing framework that significantly minimizes ray transmission and intersection overheads. Compared to state-of-the-art approaches, our method achieved a 2.29∼ 3.36× speedup with an almost imperceptible image quality loss.
Description
CCS Concepts: Computing methodologies → Computer graphics; Ray tracing; Neural networks
@inproceedings{10.2312:pg.20241287,
booktitle = {Pacific Graphics Conference Papers and Posters},
editor = {Chen, Renjie and Ritschel, Tobias and Whiting, Emily},
title = {{Data Parallel Ray Tracing of Massive Scenes based on Neural Proxy}},
author = {Xu, Shunkang and Xu, Xiang and Xu, Yanning and Wang, Lu},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-250-9},
DOI = {10.2312/pg.20241287}
}