Automatic Step Size Relaxation in Sphere Tracing

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dc.contributor.authorBán, Róberten_US
dc.contributor.authorValasek, Gáboren_US
dc.contributor.editorBabaei, Vahiden_US
dc.contributor.editorSkouras, Melinaen_US
dc.date.accessioned2023-05-03T06:03:02Z
dc.date.available2023-05-03T06:03:02Z
dc.date.issued2023
dc.description.abstractWe 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.en_US
dc.description.sectionheadersSubdivision and SDFs
dc.description.seriesinformationEurographics 2023 - Short Papers
dc.identifier.doi10.2312/egs.20231014
dc.identifier.isbn978-3-03868-209-7
dc.identifier.issn1017-4656
dc.identifier.pages57-60
dc.identifier.pages4 pages
dc.identifier.urihttps://doi.org/10.2312/egs.20231014
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egs20231014
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Ray tracing; Shape modeling
dc.subjectComputing methodologies → Ray tracing
dc.subjectShape modeling
dc.titleAutomatic Step Size Relaxation in Sphere Tracingen_US
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