TraM‐NeRF: Tracing Mirror and Near‐Perfect Specular Reflections Through Neural Radiance Fields
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
© 2024 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.
Abstract
Implicit representations like neural radiance fields (NeRF) showed impressive results for photorealistic rendering of complex scenes with fine details. However, ideal or near‐perfectly specular reflecting objects such as mirrors, which are often encountered in various indoor scenes, impose ambiguities and inconsistencies in the representation of the re‐constructed scene leading to severe artifacts in the synthesized renderings. In this paper, we present a novel reflection tracing method tailored for the involved volume rendering within NeRF that takes these mirror‐like objects into account while avoiding the cost of straightforward but expensive extensions through standard path tracing. By explicitly modelling the reflection behaviour using physically plausible materials and estimating the reflected radiance with Monte‐Carlo methods within the volume rendering formulation, we derive efficient strategies for importance sampling and the transmittance computation along rays from only few samples. We show that our novel method enables the training of consistent representations of such challenging scenes and achieves superior results in comparison to previous state‐of‐the‐art approaches.
Description
@article{10.1111:cgf.15163,
journal = {Computer Graphics Forum},
title = {{TraM‐NeRF: Tracing Mirror and Near‐Perfect Specular Reflections Through Neural Radiance Fields}},
author = {Holland, Leif Van and Bliersbach, Ruben and Müller, Jan U. and Stotko, Patrick and Klein, Reinhard},
year = {2024},
publisher = {© 2024 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.},
DOI = {10.1111/cgf.15163}
}