Browsing by Author "Jakob, Wenzel"

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    EUROGRAPHICS 2019: Tutorials Frontmatter
    (Eurographics Association, 2019) Jakob, Wenzel; Puppo, Enrico; Jakob, Wenzel and Puppo, Enrico
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    Eurographics Symposium on Rendering - Experimental Ideas and Implementations: Frontmatter
    (The Eurographics Association, 2018) Jakob, Wenzel; Hachisuka, Toshiya; Jakob, Wenzel and Hachisuka, Toshiya
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    Eurographics Symposium on Rendering: Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Jakob, Wenzel; Hachisuka, Toshiya; Jakob, Wenzel and Hachisuka, Toshiya
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    A Low-Dimensional Function Space for Efficient Spectral Upsampling
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Jakob, Wenzel; Hanika, Johannes; Alliez, Pierre and Pellacini, Fabio
    We present a versatile technique to convert textures with tristimulus colors into the spectral domain, allowing such content to be used in modern rendering systems. Our method is based on the observation that suitable reflectance spectra can be represented using a low-dimensional parametric model that is intrinsically smooth and energy-conserving, which leads to significant simplifications compared to prior work. The resulting spectral textures are compact and efficient: storage requirements are identical to standard RGB textures, and as few as six floating point instructions are required to evaluate them at any wavelength. Our model is the first spectral upsampling method to achieve zero error on the full sRGB gamut. The technique also supports large-gamut color spaces, and can be vectorized effectively for use in rendering systems that handle many wavelengths at once.
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    Material and Lighting Reconstruction for Complex Indoor Scenes with Texture-space Differentiable Rendering
    (The Eurographics Association, 2021) Nimier-David, Merlin; Dong, Zhao; Jakob, Wenzel; Kaplanyan, Anton; Bousseau, Adrien and McGuire, Morgan
    Modern geometric reconstruction techniques achieve impressive levels of accuracy in indoor environments. However, such captured data typically keeps lighting and materials entangled. It is then impossible to manipulate the resulting scenes in photorealistic settings, such as augmented / mixed reality and robotics simulation. Moreover, various imperfections in the captured data, such as missing detailed geometry, camera misalignment, uneven coverage of observations, etc., pose challenges for scene recovery. To address these challenges, we present a robust optimization pipeline based on differentiable rendering to recover physically based materials and illumination, leveraging RGB and geometry captures. We introduce a novel texture-space sampling technique and carefully chosen inductive priors to help guide reconstruction, avoiding low-quality or implausible local minima. Our approach enables robust and high-resolution reconstruction of complex materials and illumination in captured indoor scenes. This enables a variety of applications including novel view synthesis, scene editing, local & global relighting, synthetic data augmentation, and other photorealistic manipulations.
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    Neural BTF Compression and Interpolation
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Rainer, Gilles; Jakob, Wenzel; Ghosh, Abhijeet; Weyrich, Tim; Alliez, Pierre and Pellacini, Fabio
    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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    Practical Product Path Guiding Using Linearly Transformed Cosines
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Diolatzis, Stavros; Gruson, Adrien; Jakob, Wenzel; Nowrouzezahrai, Derek; Drettakis, George; Dachsbacher, Carsten and Pharr, Matt
    Path tracing is now the standard method used to generate realistic imagery in many domains, e.g., film, special effects, architecture etc. Path guiding has recently emerged as a powerful strategy to counter the notoriously long computation times required to render such images. We present a practical path guiding algorithm that performs product sampling, i.e., samples proportional to the product of the bidirectional scattering distribution function (BSDF) and incoming radiance. We use a spatial-directional subdivision to represent incoming radiance, and introduce the use of Linearly Transformed Cosines (LTCs) to represent the BSDF during path guiding, thus enabling efficient product sampling. Despite the computational efficiency of LTCs, several optimizations are needed to make our method cost effective. In particular, we show how we can use vectorization, precomputation, as well as strategies to optimize multiple importance sampling and Russian roulette to improve performance. We evaluate our method on several scenes, demonstrating consistent improvement in efficiency compared to previous work, especially in scenes with significant glossy inter-reflection.
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    Quantifying the Error of Light Transport Algorithms
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Celarek, Adam; Jakob, Wenzel; Wimmer, Michael; Lehtinen, Jaakko; Boubekeur, Tamy and Sen, Pradeep
    This paper proposes a new methodology for measuring the error of unbiased physically based rendering algorithms. The current state of the art includes mean squared error (MSE) based metrics and visual comparisons of equal-time renderings of competing algorithms. Neither is satisfying as MSE does not describe behavior and can exhibit significant variance, and visual comparisons are inherently subjective. Our contribution is two-fold: First, we propose to compute many short renderings instead of a single long run and use the short renderings to estimate MSE expectation and variance as well as per-pixel standard deviation. An algorithm that achieves good results in most runs, but with occasional outliers is essentially unreliable, which we wish to quantify numerically. We use per-pixel standard deviation to identify problematic lighting effects of rendering algorithms. The second contribution is the error spectrum ensemble (ESE), a tool for measuring the distribution of error over frequencies. The ESE serves two purposes: It reveals correlation between pixels and can be used to detect outliers, which offset the amount of error substantially.
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    Unified Neural Encoding of BTFs
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Rainer, Gilles; Ghosh, Abhijeet; Jakob, Wenzel; Weyrich, Tim; Panozzo, Daniele and Assarsson, Ulf
    Realistic rendering using discrete reflectance measurements is challenging, because arbitrary directions on the light and view hemispheres are queried at render time, incurring large memory requirements and the need for interpolation. This explains the desire for compact and continuously parametrized models akin to analytic BRDFs; however, fitting BRDF parameters to complex data such as BTF texels can prove challenging, as models tend to describe restricted function spaces that cannot encompass real-world behavior. Recent advances in this area have increasingly relied on neural representations that are trained to reproduce acquired reflectance data. The associated training process is extremely costly and must typically be repeated for each material. Inspired by autoencoders, we propose a unified network architecture that is trained on a variety of materials, and which projects reflectance measurements to a shared latent parameter space. Similarly to SVBRDF fitting, real-world materials are represented by parameter maps, and the decoder network is analog to the analytic BRDF expression (also parametrized on light and view directions for practical rendering application). With this approach, encoding and decoding materials becomes a simple matter of evaluating the network. We train and validate on BTF datasets of the University of Bonn, but there are no prerequisites on either the number of angular reflectance samples, or the sample positions. Additionally, we show that the latent space is well-behaved and can be sampled from, for applications such as mipmapping and texture synthesis.
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    Wide Gamut Spectral Upsampling with Fluorescence
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Jung, Alisa; Wilkie, Alexander; Hanika, Johannes; Jakob, Wenzel; Dachsbacher, Carsten; Boubekeur, Tamy and Sen, Pradeep
    Physically based spectral rendering has become increasingly important in recent years. However, asset textures in such systems are usually still drawn or acquired as RGB tristimulus values. While a number of RGB to spectrum upsampling techniques are available, none of them support upsampling of all colours in the full spectral locus, as it is intrinsically bigger than the gamut of physically valid reflectance spectra. But with display technology moving to increasingly wider gamuts, the ability to achieve highly saturated colours becomes an increasingly important feature. Real materials usually exhibit smooth reflectance spectra, while computationally generated spectra become more blocky as they represent increasingly bright and saturated colours. In print media, plastic or textile design, fluorescent dyes are added to extend the boundaries of the gamut of reflectance spectra. We follow the same approach for rendering: we provide a method which, given an input RGB tristimulus value, automatically provides a mixture of a regular, smooth reflectance spectrum plus a fluorescent part. For highly saturated input colours, the combination yields an improved reconstruction compared to what would be possible relying on a reflectance spectrum alone. At the core of our technique is a simple parametric spectral model for reflectance, excitation, and emission that allows for compact storage and is compatible with texture mapping. The model can then be used as a fluorescent diffuse component in an existing more complex BRDF model. We also provide importance sampling routines for practical application in a path tracer.