Automatic Step Size Relaxation in Sphere Tracing
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Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We propose a robust auto-relaxed sphere tracing method that automatically scales its step sizes based on data from previous iterations. It possesses a scalar hyperparemeter that is used similarly to the learning rate of gradient descent methods. We show empirically that this scalar degree of freedom has a smaller effect on performance than the step-scale hyperparameters of concurrent sphere tracing variants. Additionally, we compare the performance of our algorithm to these both on procedural and discrete signed distance input and show that it outperforms or performs up to par to the most efficient method, depending on the limit on iteration counts. We also verify that our method takes significantly fewer robustness-preserving sphere trace fallback steps, as it generates fewer invalid, over-relaxed step sizes.
Description
CCS Concepts: Computing methodologies → Ray tracing; Shape modeling
@inproceedings{10.2312:egs.20231014,
booktitle = {Eurographics 2023 - Short Papers},
editor = {Babaei, Vahid and Skouras, Melina},
title = {{Automatic Step Size Relaxation in Sphere Tracing}},
author = {Bán, Róbert and Valasek, Gábor},
year = {2023},
publisher = {The Eurographics Association},
ISSN = {1017-4656},
ISBN = {978-3-03868-209-7},
DOI = {10.2312/egs.20231014}
}