Rapid material capture through sparse and multiplexed measurements

Brok, Dennis den; Weinmann, Michael; Klein, Reinhard

doi:10.1016/j.cag.2018.03.003

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…Acquisition can be accelerated further when using a more principled objective function [BP20]. Similar ideas were proposed for spatially‐varying BRDFs (svBRDFs) [ZCD*16, YXM*16] and for Bi‐directional Texture Functions (BTFs) [dBWK18]. Our work differs in that it targets non‐linear, deep, representation of BRDFs.…”

Section: Previous Workmentioning

confidence: 99%

Learning to Learn and Sample BRDFs

Liu

Fischer

Ritschel

2023

Computer Graphics Forum

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We propose a method to accelerate the joint process of physically acquiring and learning neural Bi‐directional Reflectance Distribution Function (BRDF) models. While BRDF learning alone can be accelerated by meta‐learning, acquisition remains slow as it relies on a mechanical process. We show that meta‐learning can be extended to optimize the physical sampling pattern, too. After our method has been meta‐trained for a set of fully‐sampled BRDFs, it is able to quickly train on new BRDFs with up to five orders of magnitude fewer physical acquisition samples at similar quality. Our approach also extends to other linear and non‐linear BRDF models, which we show in an extensive evaluation.

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Section: Previous Workmentioning

confidence: 99%

Learning to Learn and Sample BRDFs

Liu

Fischer

Ritschel

2023

Computer Graphics Forum

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“…To this end, capable procedural approaches for the realistic rendering of glossy sparkling materials based on micro-facet theory exist [16]; however, a side-by-side comparison of their results to photographs of real materials has not been presented. Den Brok et al [17] demonstrated the possibility of accurate BTF reconstruction from sparse multiplexed measurements. Spatially localized anisotropic effects of fabrics were fitted from BTF data in Ref.…”

Section: Acquisition Of Spatially-varying Reflectancementioning

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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