Browsing by Author "Zhao, Shuang"

Now showing 1 - 8 of 8
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    A Bayesian Inference Framework for Procedural Material Parameter Estimation
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Guo, Yu; Hasan, Milos; Yan, Lingqi; Zhao, Shuang; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    Procedural material models have been gaining traction in many applications thanks to their flexibility, compactness, and easy editability. We explore the inverse rendering problem of procedural material parameter estimation from photographs, presenting a unified view of the problem in a Bayesian framework. In addition to computing point estimates of the parameters by optimization, our framework uses a Markov Chain Monte Carlo approach to sample the space of plausible material parameters, providing a collection of plausible matches that a user can choose from, and efficiently handling both discrete and continuous model parameters. To demonstrate the effectiveness of our framework, we fit procedural models of a range of materials-wall plaster, leather, wood, anisotropic brushed metals and layered metallic paints-to both synthetic and real target images.
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    Efficient Path-Space Differentiable Volume Rendering With Respect To Shapes
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Yu, Zihan; Zhang, Cheng; Maury, Olivier; Hery, Christophe; Dong, Zhao; Zhao, Shuang; Ritschel, Tobias; Weidlich, Andrea
    Differentiable rendering of translucent objects with respect to their shapes has been a long-standing problem. State-of-theart methods require detecting object silhouettes or specifying change rates inside translucent objects-both of which can be expensive for translucent objects with complex shapes. In this paper, we address this problem for translucent objects with no refractive or reflective boundaries. By reparameterizing interior components of differential path integrals, our new formulation does not require change rates to be specified in the interior of objects. Further, we introduce new Monte Carlo estimators based on this formulation that do not require explicit detection of object silhouettes.
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    Multi-Scale Appearance Modeling of Granular Materials with Continuously Varying Grain Properties
    (The Eurographics Association, 2020) Zhang, Cheng; Zhao, Shuang; Dachsbacher, Carsten and Pharr, Matt
    Many real-world materials such as sand, snow, salt, and rice are comprised of large collections of grains. Previously, multiscale rendering of granular materials requires precomputing light transport per grain and has difficulty in handling materials with continuously varying grain properties. Further, existing methods usually describe granular materials by explicitly storing individual grains, which becomes hugely data-intensive to describe large objects, or replicating small blocks of grains, which lacks the flexibility to describe materials with grains distributed in nonuniform manners. We introduce a new method to render granular materials with continuously varying grain optical properties efficiently. This is achieved using a novel symbolic and differentiable simulation of light transport during precomputation. Additionally, we introduce a new representation to depict large-scale granular materials with complex grain distributions. After constructing a template tile as preprocessing, we adapt it at render time to generate large quantities of grains with user-specified distributions. We demonstrate the effectiveness of our techniques using a few examples with a variety of grain properties and distributions.
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    Physics-Based Inverse Rendering using Combined Implicit and Explicit Geometries
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Cai, Guangyan; Yan, Kai; Dong, Zhao; Gkioulekas, Ioannis; Zhao, Shuang; Ghosh, Abhijeet; Wei, Li-Yi
    Mathematically representing the shape of an object is a key ingredient for solving inverse rendering problems. Explicit representations like meshes are efficient to render in a differentiable fashion but have difficulties handling topology changes. Implicit representations like signed-distance functions, on the other hand, offer better support of topology changes but are much more difficult to use for physics-based differentiable rendering. We introduce a new physics-based inverse rendering pipeline that uses both implicit and explicit representations. Our technique enjoys the benefit of both representations by supporting both topology changes and differentiable rendering of complex effects such as environmental illumination, soft shadows, and interreflection. We demonstrate the effectiveness of our technique using several synthetic and real examples.
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    Practical Ply-Based Appearance Modeling for Knitted Fabrics
    (The Eurographics Association, 2021) Montazeri, Zahra; Gammelmark, Søren; Jensen, Henrik Wann; Zhao, Shuang; Bousseau, Adrien and McGuire, Morgan
    Abstract Modeling the geometry and the appearance of knitted fabrics has been challenging due to their complex geometries and interactions with light. Previous surface-based models have difficulties capturing fine-grained knit geometries; Micro-appearance models, on the other hands, typically store individual cloth fibers explicitly and are expensive to be generated and rendered. Further, neither of the models offers the flexibility to accurately capture both the reflection and the transmission of light simultaneously. In this paper, we introduce an efficient technique to generate knit models with user-specified knitting patterns. Our model stores individual knit plies with fiber-level detailed depicted using normal and tangent mapping. We evaluate our generated models using a wide array of knitting patterns. Further, we compare qualitatively renderings to our models to photos of real samples.
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    Rainbow: A Rendering-Aware Index for High-Quality Spatial Scatterplots with Result-Size Budgets
    (The Eurographics Association, 2022) Bai, Qiushi; Alsudais, Sadeem; Li, Chen; Zhao, Shuang; Bujack, Roxana; Tierny, Julien; Sadlo, Filip
    We study the problem of computing a spatial scatterplot on a large dataset for arbitrary zooming/panning queries. We introduce a general framework called ''Rainbow'' that generates a high-quality scatterplot for a given result-size budget. Rainbow augments a spatial index with judiciously selected representative points offline. To answer a query, Rainbow traverses the index top-down and selects representative points with a good quality until the result-size budget is reached. We experimentally demonstrate the effectiveness of Rainbow.
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    Two-stage Photograph Cartoonization via Line Tracing
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Li, Simin; Wen, Qiang; Zhao, Shuang; Sun, Zixun; He, Shengfeng; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    Cartoon is highly abstracted with clear edges, which makes it unique from the other art forms. In this paper, we focus on the essential cartoon factors of abstraction and edges, aiming to cartoonize real-world photographs like an artist. To this end, we propose a two-stage network, each stage explicitly targets at producing abstracted shading and crisp edges respectively. In the first abstraction stage, we propose a novel unsupervised bilateral flattening loss, which allows generating high-quality smoothing results in a label-free manner. Together with two other semantic-aware losses, the abstraction stage imposes different forms of regularization for creating cartoon-like flattened images. In the second stage we draw lines on the structural edges of the flattened cartoon with the fully supervised line drawing objective and unsupervised edge augmenting loss. We collect a cartoon-line dataset with line tracing, and it serves as the starting point for preparing abstraction and line drawing data. We have evaluated the proposed method on a large number of photographs, by converting them to three different cartoon styles. Our method substantially outperforms state-of-the-art methods in terms of visual quality quantitatively and qualitatively.
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    Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Rendering
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Luan, Fujun; Zhao, Shuang; Bala, Kavita; Dong, Zhao; Bousseau, Adrien and McGuire, Morgan
    Reconstructing the shape and appearance of real-world objects using measured 2D images has been a long-standing inverse rendering problem. In this paper, we introduce a new analysis-by-synthesis technique capable of producing high-quality reconstructions through robust coarse-to-fine optimization and physics-based differentiable rendering. Unlike most previous methods that handle geometry and reflectance largely separately, our method unifies the optimization of both by leveraging image gradients with respect to both object reflectance and geometry. To obtain physically accurate gradient estimates, we develop a new GPU-based Monte Carlo differentiable renderer leveraging recent advances in differentiable rendering theory to offer unbiased gradients while enjoying better performance than existing tools like PyTorch3D [RRN*20] and redner [LADL18]. To further improve robustness, we utilize several shape and material priors as well as a coarse-to-fine optimization strategy to reconstruct geometry. Using both synthetic and real input images, we demonstrate that our technique can produce reconstructions with higher quality than previous methods.