Jiri Filip

Bidirectional Texture Function Compression Based on Multi-Level Vector Quantization

The Bidirectional Texture Function (BTF) is becoming widely used for accurate representation of real‐world material appearance. In this paper a novel BTF compression model is proposed. The model resamples input BTF data into a parametrization, allowing decomposition of individual view and illumination dependent texels into a set of multi‐dimensional conditional probability density functions. These functions are compressed in turn using a novel multi‐level vector quantization algorithm. The result of this algorithm is a set of index and scale code‐books for individual dimensions. BTF reconstruction from the model is then based on fast chained indexing into the nested stored code‐books. In the proposed model, luminance and chromaticity are treated separately to achieve further compression. The proposed model achieves low distortion and compression ratios 1:233–1:2040, depending on BTF sample variability. These results compare well with several other BTF compression methods with predefined compression ratios, usually smaller than 1:200. We carried out a psychophysical experiment comparing our method with LPCA method. BTF synthesis from the model was implemented on a standard GPU, yielded interactive framerates. The proposed method allows the fast importance sampling required by eye‐path tracing algorithms in image synthesis.

BTF image space utmost compression and modelling method

The bidirectional texture function (BTF) describes texture appearance variations due to varying illumination and viewing conditions. This function is acquired by large number of measurements for all possible combinations of illumination and viewing positions hence some compressed representation of these huge BTF texture data spaces is obviously inevitable. In this paper, we present a novel efficient probabilistic model-based method for multispectral BTF texture compression which simultaneously allows its efficient modelling. This representation model is capable of seamless BTF space enlargement and direct implementation inside the graphical card processing unit. The analytical step of the algorithm starts with BTF texture surface estimation followed by the spatial factorization of an input multispectral texture image. Single band-limited factors are independently modelled by their dedicated 3D causal autoregressive models (CAR). We estimate an optimal contextual neighbourhood and parameters for each CAR. Finally, the synthesized multiresolution multispectral texture pyramid is collapsed into the required size fine resolution synthetic smooth texture. Resulting BTF is combined in a displacement map filter of the rendering hardware using both multispectral and range information, respectively. The presented model offers immense BTF texture compression ratio which cannot be achieved by any other sampling-based BTF texture synthesis method.

Template-Based Sampling of Anisotropic BRDFs

BRDFs are commonly used to represent given materials’ appearance in computer graphics and related fields. Although, in the recent past, BRDFs have been extensively measured, compressed, and fitted by a variety of analytical models, most research has been primarily focused on simplified isotropic BRDFs. In this paper, we present a unique database of 150 BRDFs representing a wide range of materials; the majority exhibiting anisotropic behavior. Since time‐consuming BRDF measurement represents a major obstacle in the digital material appearance reproduction pipeline, we tested several approaches estimating a very limited set of samples capable of high quality appearance reconstruction. Initially, we aligned all measured BRDFs according to the location of the anisotropic highlights. Then we propose an adaptive sampling method based on analysis of the measured BRDFs. For each BRDF, a unique sampling pattern was computed, given a predefined count of samples. Further, template‐based methods are introduced based on reusing of the precomputed sampling patterns. This approach enables a more efficient measurement of unknown BRDFs while preserving the visual fidelity for the majority of tested materials. Our method exhibits better performance and stability than competing sparse sampling approaches; especially for higher numbers of samples.

Fast Synthesis of Dynamic Colour Textures

Textural appearance of many real word materials is not static but shows progress in time. If such a progress is spatially and temporally homogeneous these materials can be represented by means of dynamic texture (DT). DT modelling is a challenging problem which can add new quality into computer graphics applications. We propose a novel hybrid method for colour DTs modelling. The method is based on eigen-analysis of DT images and subsequent preprocessing and modelling of temporal interpolation eigen-coefficients using a causal auto-regressive model. The proposed method shows good performance for most of the tested DTs, which depends mainly on the properties of the original sequence. Moreover, this method compresses significantly the original data and enables extremely fast synthesis of artificial sequence, which can be easily performed by means of contemporary graphics hardware

BRDF Slices: Accurate Adaptive Anisotropic Appearance Acquisition

In this paper we introduce unique publicly available dense an isotropic BRDF data measurements. We use this dense data as a reference for performance evaluation of the proposed BRDF sparse angular sampling and interpolation approach. The method is based on sampling of BRDF subspaces at fixed elevations by means of several adaptively-represented, uniformly distributed, perpendicular slices. Although this proposed method requires only a sparse sampling of material, the interpolation provides a very accurate reconstruction, visually and computationally comparable to densely measured reference. Due to the simple slices measurement and methods robustness it allows for a highly accurate acquisition of BRDFs. This in comparison with standard uniform angular sampling, is considerably faster yet uses far less samples.

Non-linear reflectance model for bidirectional texture function synthesis

A rough texture modelling involves a huge image data-set - the bidirectional texture function (BTF). This 6-dimensional function depends on planar texture coordinates as well as on view and illumination angles. We propose a new non-linear reflectance model, based on a Lafortune reflectance model improvement, which restores all BTF database images independently for each view position and herewith significantly reduces stored BTF data size. The extension consists in introducing several spectral parameters for each BTF image which are linearly estimated in the second estimation step according to the original data. The model parameters are computed for every surface reflectance field contained in the original BFT data. This technique allows BTF data compression by the ratio 1:15 while the synthesised images are almost indiscernible from the originals. The method is universal, and easily implementable in a graphical hardware for the purpose of real-time BTF rendering.

Effective Acquisition of Dense Anisotropic BRDF

The development of novel analytical BRDF models, as well as adaptive BRDF sampling approaches, rely on the appropriate BRDF measurement of real materials. The quality of measurements is even more critical when it comes to accurately representing anisotropic materials where the character of anisotropy is unknown (locations of anisotropic highlights, their width, shape, etc.). As currently there is a lack of dense yet noise-free BRDF anisotropic datasets, we introduce such unique measurements of three anisotropic fabric materials. In this paper we discuss a method of dense BRDF data acquisition, post processing, missing values interpolation, and analyze properties of the datasets. Our results are compared with photographs, dense data fitted and generated by two state-of-the art anisotropic BRDF models, and alternative measurements available.

Fast restoration of colour movie scratches

Presents a new type of scratch removal algorithm based on a causal adaptive multidimensional multitemporal prediction. The predictor use available information from the neighbourhood of a missing multispectral pixels due to spectral, temporal and spatial correlation of video data but not any information from the failed pixels themselves.

Perceptually motivated BRDF comparison using single image

Surface reflectance of real‐world materials is now widely represented by the bidirectional reflectance distribution function (BRDF) and also by spatially varying representations such as SVBRDF and the bidirectional texture function (BTF). The raw surface reflectance measurements are typically compressed or fitted by analytical models, that always introduce a certain loss of accuracy. For its evaluation we need a distance function between a reference surface reflectance and its approximate version. Although some of the past techniques tried to reflect the perceptual sensitivity of human vision, they have neither optimized illumination and viewing conditions nor surface shape. In this paper, we suggest a new image‐based methodology for comparing different anisotropic BRDFs. We use optimization techniques to generate a novel surface which has extensive coverage of incoming and outgoing light directions, while preserving its features and frequencies that are important for material appearance judgments. A single rendered image of such a surface along with simultaneously optimized lighting and viewing directions leads to the computation of a meaningful BRDF difference, by means of standard image difference predictors. A psychophysical experiments revealed that our surface provides richer information on material properties than the standard surfaces often used in computer graphics, e.g., sphere or blob.

BTF modelling using BRDF texels

The highest fidelity representations of realistic real-world materials currently comprise Bidirectional Texture Functions (BTF). The BTF is a six-dimensional function depending on view and illumination directions as well as on planar texture coordinates. The huge size of such measurements, typically in the form of thousands of images covering all possible combinations of illumination and viewing angles, has prohibited their practical exploitation, and obviously some compression and modelling method of these enormous BTF data spaces is inevitable. The two proposed approaches combine BTF spatial clustering with cluster index modelling by means of efficient Markov random field models. The methods allow the generation of a seamless cluster index of arbitrary size to cover large virtual 3D object surfaces. Both methods represent original BTF data using a set of local spatially dependent Bidirectional Reflectance Distribution Function (BRDF) values which are combined according to the synthesized cluster index and illumination/viewing directions by means of two types of Markov random field models. BTF data compression using both methods is about 1:200 and their synthesis is very fast.