A General BRDF Representation Based on Tensor Decomposition

Bilgili, Ahmet; Öztürk, A.; Kurt, Murat

doi:10.1111/j.1467-8659.2011.02072.x

Cited by 36 publications

(39 citation statements)

References 53 publications

(110 reference statements)

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“…It performs well, but its computational demands increase dramatically when the number of samples is in the order of thousands, and therefore factorization is required. An alternative approach to BRDF data interpolation is to use data factorization by Tucker decomposition [18]. This can represent both isotropic and anisotropic BRDFs with quality comparable to analytic BRDF models in reasonable reconstruction time.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive slices for acquisition of anisotropic BRDF

Vávra

Filip

2018

Comp. Visual Media

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BRDF continues to be used as a fundamental tool for representing material appearance in computer graphics. In this paper we present a practical adaptive method for acquisition of anisotropic BRDF, based on sparse adaptive measurement of the complete four-dimensional BRDF space by means of one-dimensional slices, which form a sparse fourdimensional structure in the BRDF space, and can be measured by continuous movements of a light source and sensor. Such a sampling approach is advantageous especially for gonioreflectometer-based measurement devices where the mechanical travel of a light source and a sensor imposes a significant time constraint. In order to evaluate our method, we have performed adaptive measurements of three materials and we simulated adaptive measurements of thirteen others. This method has one quarter the reconstruction error of that resulting from regular non-adaptive BRDF measurements using the same number of measured samples. Our method is almost twice as good as a previous adaptive method, and it requires from two to five times fewer samples to achieve the same results as alternative approaches.

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Section: Related Workmentioning

confidence: 99%

“…Improvement in the MRE due to usage of the proposed method instead of barycentric or RBF interpolation for sampling schemes 14 (8911 samples) and 19 (18,721 samples) is shown in Fig. 9.…”

Section: Simulated Measurement Experimentsmentioning

confidence: 99%

Adaptive slices for acquisition of anisotropic BRDF

Vávra

Filip

2018

Comp. Visual Media

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“…In graphics, tensor decompositions have been used for example for compact global illumination models like bidirectional reflectance distribution functions (e.g., [8,24,33]), for bidirectional texture functions (e.g., [32,37,40,41]), or for texture synthesis (see [41]). In DVR only the Tucker model has been applied so far (see [28,29,30]).…”

Section: Tensor Approximation Modelsmentioning

confidence: 99%

Analysis of tensor approximation for compression-domain volume visualization

Ballester-Ripoll

Suter

Pajarola

2015

Computers & Graphics

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As modern high-resolution imaging devices allow to acquire increasingly large and complex volume data sets, their effective and compact representation for visualization becomes a challenging task. The Tucker decomposition has already confirmed higher-order tensor approximation (TA) as a viable technique for compressed volume representation; however, alternative decomposition approaches exist. In this work, we review the main TA models proposed in the literature on multiway data analysis and study their application in a visualization context, where reconstruction performance is emphasized along with reduced data representation costs. Progressive and selective detail reconstruction is a main goal for such representations and can efficiently be achieved by truncating an existing decomposition. To this end, we explore alternative incremental variations of the CANDECOMP/PARAFAC and Tucker models. We give theoretical time and space complexity estimates for every discussed approach and variant. Addi-tionally, their empirical decomposition and reconstruction times and approximation quality are tested in both C++ and MATLAB implementations. Several scanned real-life exemplar volumes are used varying data sizes, initialization methods, degree of com-pression and truncation. As a result of this, we demonstrate the superiority of the Tucker model for most visualization purposes, while canonical-based models offer benefits only in limited situations. AbstractAs modern high-resolution imaging devices allow to acquire increasingly large and complex volume data sets, their effective and compact representation for visualization becomes a challenging task. The Tucker decomposition has already confirmed higherorder tensor approximation (TA) as a viable technique for compressed volume representation; however, alternative decomposition approaches exist. In this work, we review the main TA models proposed in the literature on multiway data analysis and study their application in a visualization context, where reconstruction performance is emphasized along with reduced data representation costs. Progressive and selective detail reconstruction is a main goal for such representations and can efficiently be achieved by truncating an existing decomposition. To this end, we explore alternative incremental variations of the CANDECOMP/PARAFAC and Tucker models. We give theoretical time and space complexity estimates for every discussed approach and variant. Additionally, their empirical decomposition and reconstruction times and approximation quality are tested in both C++ and MATLAB implementations. Several scanned real-life exemplar volumes are used varying data sizes, initialization methods, degree of compression and truncation. As a result of this, we demonstrate the superiority of the Tucker model for most visualization purposes, while canonical-based models offer benefits only in limited situations.

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“…Partial model methods attempt to separate each BRDF into a product of lower-dimensional functions that can be fit to measured data (McCool, 2001;Bilgili et al, 2011;Suykens et al, 2003). This approach stands somewhere between table-based methods and analytical BRDF models (Cook and Torrance, 1981;He et al;War92,LaFortune et al, 1997;Edwards et al 2006).…”

Section: Background and Relatedworkmentioning

confidence: 99%

A Practical Framework for Sharing and Rendering Real-World Bidirectional Scattering Distribution Functions

Ward¹

2012

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The utilization of real-world materials has been hindered by a lack of standards for sharing and interpreting measured data. This paper presents an XML representation and an Open Source C library to support bidirectional scattering distribution functions (BSDFs) in data-driven lighting simulation and rendering applications.The library provides for the efficient representation, query, and Monte Carlo sampling of arbitrary BSDFs in amodel-free framework. Currently, we support two BSDF data representations: one using a fixed subdivision of thehemisphere, and one with adaptive density. The fixed type has advantages for certain matrix operations, while theadaptive type can more accurately represent highly peaked data. We discuss advanced methods for data-drivenBSDF rendering for both types, including the proxy of detailed geometry to enhance appearance and accuracy.We also present an advanced interpolation method to reduce measured data into these standard representations.We end with our plan for future extensions and sharing of BSDF data.

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A General BRDF Representation Based on Tensor Decomposition

Cited by 36 publications

References 53 publications

Adaptive slices for acquisition of anisotropic BRDF

Adaptive slices for acquisition of anisotropic BRDF

Analysis of tensor approximation for compression-domain volume visualization

A Practical Framework for Sharing and Rendering Real-World Bidirectional Scattering Distribution Functions

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