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)
报告嘉宾：潘高乐(南京理工大学)
报告时间：2024年3月14号星期四晚上8:00-8:30（北京时间）
报告题目：Multiple-bounce Smith Microfacet BRDFs using the Invariance Principle
报告摘要：
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.
讲者简介：
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。
报告嘉宾：李泽儒(阿里巴巴)
报告时间：2024年3月14号星期四晚上8:30-9:00（北京时间）
报告题目：MetaLayer: A Meta-learned BSDF Model for Layered Materials
报告摘要：
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.
讲者简介：
李泽儒，现阿里巴巴淘系技术渲染引擎工程师，毕业于南京大学，导师为郭延文教授和过洁研究员，主要研究方向为真实感渲染中的材质。
主持人简介：
霍宇驰，浙江大学CAD&CG实验室百人计划研究员，专注于光传输相关的计算机图形学、计算机视觉、计算光学、人工智能的研究工作，当前的研究题目包括真实感绘制、实时绘制、神经绘制、三维重建、光学神经网络等。
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观看直播的链接： https://live.bilibili.com/h5/24617282
