GAMES Webinar 2024 – 314期(真实感材质表达的物理与神经方法) | 潘高乐(南京理工大学),李泽儒(阿里巴巴)

【GAMES Webinar 2024-314期】(渲染专题-真实感材质表达的物理与神经方法)



报告题目:Multiple-bounce Smith Microfacet BRDFs using the Invariance Principle


Smith microfacet models are widely used in computer graphics to represent materials. Traditional microfacet models do not consider the multiple bounces on microgeometries, leading to visible energy missing, especially on rough surfaces. Later, as the equivalence between the microfacets and volume has been revealed, random walk solutions have been proposed to introduce multiple bounces, but at the cost of high variance. Recently, the position-free property has been introduced into the multiple-bounce model, resulting in much less noise, but also bias or a complex derivation. In this paper, we propose a simple way to derive the multiple-bounce Smith microfacet bidirectional reflectance distribution functions (BRDFs) using the invariance principle. At the core of our model is a shadowing-masking function for a path consisting of direction collections, rather than separated bounces. Our model ensures unbiasedness and can produce less noise compared to the previous work with equal time, thanks to the simple formulation. Furthermore, we also propose a novel probability density function (PDF) for BRDF multiple importance sampling, which has a better match with the multiple-bounce BRDFs, producing less noise than previous naive approximations.


潘高乐,南京理工大学PCA Lab硕士二年级,导师是王贝贝教授,主要研究方向是真实感渲染。



报告题目:MetaLayer: A Meta-learned BSDF Model for Layered Materials


Reproducing the appearance of arbitrary layered materials has long been a critical challenge in computer graphics, with regard to the demanding requirements of both physical accuracy and low computation cost. Recent studies have demonstrated promising results by learning-based representations that implicitly encode the appearance of complex (layered) materials by neural networks. However, existing generally-learned models often struggle between strong representation ability and high runtime performance, and also lack physical/perceptual parameters for intuitive material editing. To address these concerns, we introduce MetaLayer, a new methodology leveraging meta-learning for modeling and rendering layered materials. MetaLayer contains two networks: a BSDFNet that compactly encodes the appearance of layered materials, and a MetaNet that establishes the mapping between the physical parameters of each material and the weights of its corresponding implicit neural representation. A new positional encoding method and a well-designed training strategy are employed to improve the performance and quality of the neural model. As a new learning-based representation, the proposed MetaLayer model provides both fast responses to material editing and high-quality results for a wide range of layered materials, outperforming existing layered BSDF models.





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