Neural Volumetric Level of Detail for Path Tracing
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We introduce a neural level of detail pipeline for use in a GPU path tracer based on a sparse volumetric representation derived from neural radiance fields. We pre-compute lighting and occlusion to train a neural radiance field which faithfully captures appearance and shading via image-based optimization. By converting the resulting neural network into an efficiently rendered representation, we eliminate costly evaluations at runtime and keep performance competitive. When applying our representation to certain areas of the scene, we trade a slight bias from gradient-based optimization and lossy volumetric conversion for highly anti-aliased results at low sample counts. This enables virtually noise-free and temporally stable results at low computational cost and without any additional post-processing, such as denoising. We demonstrate the applicability of our method to both individual objects and a challenging outdoor scene composed of highly detailed foliage.
Description
CCS Concepts: Computing methodologies → Volumetric models; Neural networks; Antialiasing; Ray tracing
@inproceedings{10.2312:vmv.20241197,
booktitle = {Vision, Modeling, and Visualization},
editor = {Linsen, Lars and Thies, Justus},
title = {{Neural Volumetric Level of Detail for Path Tracing}},
author = {Stadter, Linda and Hofmann, Nikolai and Stamminger, Marc},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-247-9},
DOI = {10.2312/vmv.20241197}
}