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Title: Perceptually validated cross-renderer analytical BRDF parameter remapping
Authors: Guarnera, Dar'ya
Guarnera, Giuseppe C.
Toscani, Matteo
Glencross, Mashhuda
Li, Baihua
Hardeberg, Jon Y.
Gegenfurtner, Karl
Keywords: BRDF model
Perceptual validation
Virtual materials
Surface perception
Issue Date: 2018
Publisher: © IEEE
Citation: GUARNERA, D. ... et al, 2018. Perceptually validated cross-renderer analytical BRDF parameter remapping. IEEE Transactions on Visualization and Computer Graphics, doi: 10.1109/TVCG.2018.2886877.
Abstract: Material appearance of rendered objects depends on the underlying BRDF implementation used by rendering software packages. A lack of standards to exchange material parameters and data (between tools) means that artists in digital 3D prototyping and design, manually match the appearance of materials to a reference image. Since their effect on rendered output is often non-uniform and counter intuitive, selecting appropriate parameterisations for BRDF models is far from straightforward. We present a novel BRDF remapping technique, that automatically computes a mapping (BRDF Difference Probe) to match the appearance of a source material model to a target one. Through quantitative analysis, four user studies and psychometric scaling experiments, we validate our remapping framework and demonstrate that it yields a visually faithful remapping among analytical BRDFs. Most notably, our results show that even when the characteristics of the models are substantially different, such as in the case of a phenomenological model and a physically-based one, our remapped renderings are indistinguishable from the original source model.
Description: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Version: Accepted for publication
DOI: 10.1109/TVCG.2018.2886877
URI: https://dspace.lboro.ac.uk/2134/36699
Publisher Link: https://doi.org/10.1109/TVCG.2018.2886877
ISSN: 1077-2626
Appears in Collections:Published Articles (Computer Science)

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