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Neural Shadow Mapping

Authors :
Datta, Sayantan
Nowrouzezahrai, Derek
Schied, Christoph
Dong, Zhao
Source :
ACM SIGGRAPH 2022 Conference Proceedings
Publication Year :
2023

Abstract

We present a neural extension of basic shadow mapping for fast, high quality hard and soft shadows. We compare favorably to fast pre-filtering shadow mapping, all while producing visual results on par with ray traced hard and soft shadows. We show that combining memory bandwidth-aware architecture specialization and careful temporal-window training leads to a fast, compact and easy-to-train neural shadowing method. Our technique is memory bandwidth conscious, eliminates the need for post-process temporal anti-aliasing or denoising, and supports scenes with dynamic view, emitters and geometry while remaining robust to unseen objects.<br />Comment: Project Page: https://sayan1an.github.io/neuralShadowMapping.html

Subjects

Subjects :
Computer Science - Graphics

Details

Database :
arXiv
Journal :
ACM SIGGRAPH 2022 Conference Proceedings
Publication Type :
Report
Accession number :
edsarx.2301.05262
Document Type :
Working Paper
Full Text :
https://doi.org/10.1145/3528233.3530700