Adaptive slices for acquisition of anisotropic BRDF

Vávra, Radomír; Filip, Jiřı́

doi:10.1007/s41095-017-0099-z

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…[10] for predicting the global appearances of effect-pigmented surfaces. Several methods exist for sparse acquisition of anisotropic BRDFs, either using minimal sampling in combination with a precomputed linear basis [11], or adaptive sampling along 1D slices in BRDF space [12].…”

Section: Brdf Parameterizationsmentioning

confidence: 99%

Image-based appearance acquisition of effect coatings

Filip

Vávra

2019

Comp. Visual Media

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Paint manufacturers strive to introduce unique visual effects to coatings in order to visually communicate functional properties of products using value-added, customized design. However, these effects often feature complex, angularly dependent, spatiallyvarying behavior, thus representing a challenge in digital reproduction. In this paper we analyze several approaches to capturing spatially-varying appearances of effect coatings. We compare a baseline approach based on a bidirectional texture function (BTF) with four variants of half-difference parameterization. Through a psychophysical study, we determine minimal sampling along individual dimensions of this parameterization. We conclude that, compared to BTF, bivariate representations better preserve visual fidelity of effect coatings, better characterizing near-specular behavior and significantly the restricting number of images which must be captured.

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Section: Brdf Parameterizationsmentioning

confidence: 99%

Image-based appearance acquisition of effect coatings

Filip

Vávra

2019

Comp. Visual Media

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“…The development of new methods for appearance capture has led to an increasing amount of densely sampled data from realworld appearance [MPBM03,VF18,DJ18]. Although tabulated representations of reflectance data are usually very accurate, they suffer from a high memory footprint and computational cost at evaluation time [HGC*20].…”

Section: Introductionmentioning

confidence: 99%

Neural BRDF Representation and Importance Sampling

Sztrajman

Rainer

Ritschel

et al. 2021

Computer Graphics Forum

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Controlled capture of real-world material appearance yields tabulated sets of highly realistic reflectance data. In practice, however, its high memory footprint requires compressing into a representation that can be used efficiently in rendering while remaining faithful to the original. Previous works in appearance encoding often prioritized one of these requirements at the expense of the other, by either applying high-fidelity array compression strategies not suited for efficient queries during rendering, or by fitting a compact analytic model that lacks expressiveness. We present a compact neural network-based representation of BRDF data that combines high-accuracy reconstruction with efficient practical rendering via built-in interpolation of reflectance. We encode BRDFs as lightweight networks, and propose a training scheme with adaptive angular sampling, critical for the accurate reconstruction of specular highlights. Additionally, we propose a novel approach to make our representation amenable to importance sampling: rather than inverting the trained networks, we learn to encode them in a more compact embedding that can be mapped to parameters of an analytic BRDF for which importance sampling is known. We evaluate encoding results on isotropic and anisotropic BRDFs from multiple real-world datasets, and importance sampling performance for isotropic BRDFs mapped to two different analytic models.

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“…Another BRDF database which has many anisotropic BRDFs is from UTIA. They provided 150 coarsely sampled BRDFs[83] and several dense anisotropic measurements[33,84,85]. A learning-based BRDF sampling proposed by Nielsen et al[41] provided 8 reconstructed BRDFs using 20 samples.…”

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confidence: 99%

Data-Driven Approaches for Sparse Reflectance Modeling and Acquisition

Tongbuasirilai

2023

Linköping Studies in Science and Technology. Dissertations

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Description of the cover imageThe front cover of this thesis illustrates BRDFs in the form of multiple rendered spheres along BRDF curves (RGB signals) where the glossy BRDF curves are placed above the diffuse BRDF curves. The front illustration portrays the results of the thesis contents. The blue wave signals in the back cover represent sparse signals which serve as the BRDF encoding process (thesis's contributions) to reconstruct the BRDF signals in the front cover. The orange abstract waves in the background indicate that the BRDF is a type of signals around us. The materials used to render all the spheres are measured isotropic BRDFs from Mitsubishi Electric Research Laboratories (MERL).

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Adaptive slices for acquisition of anisotropic BRDF

Cited by 3 publications

References 21 publications

Image-based appearance acquisition of effect coatings

Image-based appearance acquisition of effect coatings

Neural BRDF Representation and Importance Sampling

Data-Driven Approaches for Sparse Reflectance Modeling and Acquisition

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