NeRF-Tex: Neural Reflectance Field Textures
Hendrik Baatz, Jonathan Granskog, Marios Papas, Fabrice Rousselle, Jan Nov{\'a}k
6/22/2021
Keywords: Material/Lighting Estimation, Global Conditioning, Hybrid Geometry Representation
Venue: EGSR 2021
Bibtex:
@article{baatz2021nerftex,
journal = {Computer Graphics Forum},
title = {NeRF-Tex: Neural Reflectance Field Textures},
author = {Hendrik Baatz and Jonathan Granskog and Marios Papas and Fabrice Rousselle and Jan Nov{\'a}k},
year = {2021},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
entrytype = {article},
id = {baatz2021nerftex}
}
Abstract
We investigate the use of neural fields for modeling diverse mesoscale structures, such as fur, fabric, and grass. Instead of using classical graphics primitives to model the structure, we propose to employ a versatile volumetric primitive represented by a neural reflectance field (NeRF-Tex), which jointly models the geometry of the material and its response to lighting. The NeRF-Tex primitive can be instantiated over a base mesh to''texture''it with the desired meso and microscale appearance. We condition the reflectance field on user-defined
Citation Graph
(Double click on nodes to open corresponding papers' pages)
(Double click on nodes to open corresponding papers' pages)
* Showing citation graph for papers within our database. Data retrieved from Semantic Scholar. For full citation graphs, visit ConnectedPapers.