Azimuth-rotated vector quantization for BTF compression

Kawai, Naoki; Matsufuji, Kazuo

doi:10.1145/1179622.1179768

Cited by 2 publications

(3 citation statements)

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“…Although all the above mentioned methods allow very fast BTF rendering, they mostly achieve relatively moderate compression ratios, less than 1:200. Only some of them allow fast GPU implementation, only two ([LM01, KM06]) use a variant of vector quantization, and only a few of them allow importance sampling of BTF without reconstruction. Our method is designed to provide all the features required for CPU‐based and GPU‐based rendering algorithms.…”

Section: Previous Workmentioning

confidence: 99%

“…This method was also applied for compression of psychophysically reduced BTF data in [FCGH08]. Another BTF vector quantization approach based on azimuthal rotation of resampled data F x was mentioned in [KM06]. In [LM01] introduced a BTF recognition method that captured surface appearance under different illumination and viewing conditions by using three‐dimensional (3D) textons constructed by means of K‐means clustering of responses to selective linear filters applied at individual planar positions in BTF.…”

Section: Previous Workmentioning

confidence: 99%

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Bidirectional Texture Function Compression Based on Multi‐Level Vector Quantization

Havran

Filip

Myszkowski

2010

Computer Graphics Forum

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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.

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Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Bidirectional Texture Function Compression Based on Multi‐Level Vector Quantization

Havran

Filip

Myszkowski

2010

Computer Graphics Forum

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“…Combining factorization with a clustering method like K‐means [MMK03, TZL * 02] applied to the latent representation enables the use of fewer coefficients per cluster. Other factorization techniques include hierarchical tensor decomposition methods applied to the high dimensional BTF [WWS * 05, RK09] and vector quantization methods based on codebooks [KM06,HFM10, EV14].…”

Section: Related Workmentioning

confidence: 99%

Neural BTF Compression and Interpolation

Rainer

Jakob

Ghosh

et al. 2019

Computer Graphics Forum

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The Bidirectional Texture Function (BTF) is a data‐driven solution to render materials with complex appearance. A typical capture contains tens of thousands of images of a material sample under varying viewing and lighting conditions. While capable of faithfully recording complex light interactions in the material, the main drawback is the massive memory requirement, both for storing and rendering, making effective compression of BTF data a critical component in practical applications. Common compression schemes used in practice are based on matrix factorization techniques, which preserve the discrete format of the original dataset. While this approach generalizes well to different materials, rendering with the compressed dataset still relies on interpolating between the closest samples. Depending on the material and the angular resolution of the BTF, this can lead to blurring and ghosting artefacts. An alternative approach uses analytic model fitting to approximate the BTF data, using continuous functions that naturally interpolate well, but whose expressive range is often not wide enough to faithfully recreate materials with complex non‐local lighting effects (subsurface scattering, inter‐reflections, shadowing and masking…). In light of these observations, we propose a neural network‐based BTF representation inspired by autoencoders: our encoder compresses each texel to a small set of latent coefficients, while our decoder additionally takes in a light and view direction and outputs a single RGB vector at a time. This allows us to continuously query reflectance values in the light and view hemispheres, eliminating the need for linear interpolation between discrete samples. We train our architecture on fabric BTFs with a challenging appearance and compare to standard PCA as a baseline. We achieve competitive compression ratios and high‐quality interpolation/extrapolation without blurring or ghosting artifacts.

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Azimuth-rotated vector quantization for BTF compression

Cited by 2 publications

References 3 publications

Bidirectional Texture Function Compression Based on Multi‐Level Vector Quantization

Bidirectional Texture Function Compression Based on Multi‐Level Vector Quantization

Neural BTF Compression and Interpolation

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