A wavelet-based framework for acquired radiometric quantity representation and accurate physical rendering

Claustres, Luc; Paulin, Mathias; Boucher, Yann

doi:10.1007/s00371-006-0001-y

Cited by 6 publications

(7 citation statements)

References 59 publications

(65 reference statements)

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“…Xu and Sun presented an efficient method which compressed the BRDFs in angular and spectral domains, but it could not handle a high‐frequency material or lighting because of its sparsity of data in angular and spectral domains. Claustres et al . represented spectral BRDFs in both angular and spectral domains using wavelets.…”

Section: Background and The Previous Workmentioning

confidence: 99%

“…Xu and Sun 14 presented an efficient method which compressed the BRDFs in angular and spectral domains, but it could not handle a high-frequency material or lighting because of its sparsity of data in angular and spectral domains. Claustres et al 25 represented spectral BRDFs in both angular and spectral domains using wavelets. However, the method removed the wavelet coefficient lower than the given threshold and introduced a sparse array to save the remained coefficients, which leads to inaccuracy and computational inefficiency.…”

Section: Data-driven Representationmentioning

confidence: 99%

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Recovery and representation of spectral bidirectional reflectance distribution function from an image‐based measurement system

Kim

Jang

Choi

et al. 2015

Color Research & Application

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A spectral-based method can acquire and represent the surface appearance of a given material physically correctly. But, it has drawbacks due to its high measurement cost and a long computation time in measuring, modeling, and rendering. In this article, we present spectral recovery and representation of spectral bidirectional reflectance distribution function (BRDF) from multispectral reflectance measurements in which we can render real appearance materials over a 3D model with accuracy and efficiency. First of all, an accurate spectral BRDF recovery algorithm, which transforms multispectral high dynamic range images into highly dense BRDFs in both a spectral and an angular domain, is proposed. Second, an efficient representation method is developed representing spectral BRDFs compactly using a factorization method and an adaptive spectral sampling method that uses a given error bound. The results show that the proposed method can compress the spectral BRDF data down by several hundred times while maintaining the given accuracy in colorimetric and spectral domains under a specific illuminant.

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Section: Background and The Previous Workmentioning

confidence: 99%

Section: Data-driven Representationmentioning

confidence: 99%

Recovery and representation of spectral bidirectional reflectance distribution function from an image‐based measurement system

Kim

Jang

Choi

et al. 2015

Color Research & Application

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“…At last, the BRDF samples are filtered in order to produce a smooth lighting solution (5.4.2). This complete pipeline allows modelling and real-time rendering of acquired materials in common graphics applications (6).…”

Section: Overview Of Our Contributionmentioning

confidence: 99%

“…Claustres et al have presented in [6] a wavelet compression scheme that generates high compression rates thanks to the computation of an adaptive (local) threshold for each dependence of the BRDF, which is reused in this work. The main idea consists in removing the hemispherical-to-hemispherical BRDF correlation between incoming directions in addition to standard wavelet compression.…”

Section: Compressionmentioning

confidence: 99%

Wavelet encoding of BRDFs for real-time rendering

Claustres

Barthe

Paulin

2007

Proceedings of Graphics Interface 2007 on - GI '07

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Figure 1: An illuminated fabric with an acquired anisotropic velvet (left), isotropic wood (middle) and shiny plastic (right) BRDF rendered at 40 FPS (512 × 512) using our wavelet encoding. The initial data sets containing 32 4 RGB samples (12MB) are compressed into 3D textures of 700KB. Our approach can be combined with classical texture, environment and bump mapping in order to produce high quality local illumination. ABSTRACTAcquired data often provides the best knowledge of a material's bidirectional reflectance distribution function (BRDF). Its integration into most real-time rendering systems requires both data compression and the implementation of the decompression and filtering stages on contemporary graphics processing units (GPUs). This paper improves the quality of real-time per-pixel lighting on GPUs using a wavelet decomposition of acquired BRDFs. Threedimensional texture mapping with indexing allows us to efficiently compress the BRDF data by exploiting much of the coherency between hemispherical data. We apply built-in hardware filtering and pixel shader flexibility to perform filtering in the full 4D BRDF domain. Anti-aliasing of specular highlights is performed via a progressive level-of-detail technique built upon the multiresolution of the wavelet encoding. This technique increases rendering performance on distant surfaces while maintaining accurate appearance of close ones.

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“…Up to our knowledge, compressing PS has not yet been much studied. Lots of works have been done in some related problems such as compressing Bidirectionnal Reflectance Distribution functions (BRDF) [SAWG91] [CPB06][NP00], and for approximating 3D shapes out of large point clouds [WSC06]. In the latter, the goal is not the same as ours since the purpose is to allow the shape to be rendered through its new representation using potentially cost effective algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Two Machine Learning Approaches for Photometric Solids Compression

Delepoulle

Rousselle

Renaud

et al. 2010

Intelligent Computer Graphics 2010

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The use of photometric solids into both real time and photorealistic rendering allows designers and computer artists to enhance easily the quality of their images. Lots of such data are available from lighting societies since they allow these societies to easily present the luminance distribution of their often complex ligthing systems. When accuracy is required the amount of discretized luminance directions and the number of photometric solids that have to be used increase considerably the memory requirements and reduce the algorithm efficiency. In this paper we describe and compare two machine learning approaches used for approximating any photometric solid: an artificial neural network and ECON (Equi-Correlated Network Algorithm). By applying these two approaches on a large set of real photometric distribution data, we were able to show that one of them provides generally a better approximation of the original distribution.

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A wavelet-based framework for acquired radiometric quantity representation and accurate physical rendering

Cited by 6 publications

References 59 publications

Recovery and representation of spectral bidirectional reflectance distribution function from an image‐based measurement system

Recovery and representation of spectral bidirectional reflectance distribution function from an image‐based measurement system

Wavelet encoding of BRDFs for real-time rendering

A Comparison of Two Machine Learning Approaches for Photometric Solids Compression

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