GAMES Webinar 2021 – 208期(渲染专题) | 朱君秋 (山东大学)，马晓鹤（浙江大学）
【GAMES Webinar 2021-208期】(渲染专题)
Complex luminaires, such as grand chandeliers, can be extremely costly to render because the light-emitting sources are typically encased in complex refractive geometry, creating difficult light paths that require many samples to evaluate with Monte Carlo approaches. Previous work has attempted to speed up this process, but the methods are either inaccurate, require the storage of very large lightfields, and/or do not fit well into modern path-tracing frameworks. Inspired by the success of deep networks, which can model complex relationships robustly and be evaluated efficiently, we propose to use a machine learning framework to compress a complex luminaire’s lightfield into an implicit neural representation. Our approach can easily plug into conventional renderers, as it works with the standard techniques of path tracing and multiple importance sampling (MIS). Our solution is to train three networks to perform the essential operations for evaluating the complex luminaire at a specific point and view direction, importance sampling a point on the luminaire given a shading location, and blending to determine the transparency of luminaire queries to properly composite them with other scene elements. We perform favorably relative to state-of-the-art approaches and render final images that are close to the high-sample-count reference with only a fraction of the computation and storage costs, with no need to store the original luminaire geometry and materials.
报告题目：Free-form Scanning of Non-planar Appearance with Neural Trace Photography
We propose neural trace photography, a novel framework to automatically learn high-quality scanning of non-planar, complex anisotropic appearance. Our key insight is that free-form appearance scanning can be cast as a geometry learning problem on unstructured point clouds, each of which represents an image measurement and the corresponding acquisition condition. Based on this connection, we carefully design a neural network, to jointly optimize the lighting conditions to be used in acquisition, as well as the spatially independent reconstruction of reflectance from corresponding measurements. Our framework is not tied to a specific setup, and can adapt to various factors in a data-driven manner. We demonstrate the effectiveness of our framework on a number of physical objects with a wide variation in appearance. The objects are captured with a light-weight mobile device, consisting of a single camera and an RGB LED array. We also generalize the framework to other common types of light sources, including a point, a linear and an area light.
Xiaohe Ma is 23 years old. She received her bachelor degree of engineering from Sun Yat-sen University in 2019. Currently she is a third year Ph.D. student in the State Key Lab of CAD&CG, Zhejiang University. Her research interests include appearance acquisition and modeling.
王贝贝，南京理工大学，副教授，硕士生导师，中国计算机学会CAD&CG专委会委员。研究方向是计算机图形学渲染方向，包括了全局光照算法、参与性介质光线传递和复杂材质模型等。王贝贝分别于2009年、2014年在山东大学获得学士、博士学位，期间在巴黎高科进行两年联合培养。2015年在英国游戏公司Studio Gobo参与Disney游戏Infinity 3的研发。2015年底到2017年初，在INRIA（法国信息与自动化研究所）从事博士后研究。之后加入到南京理工大学。共发表高水平论文30余篇，其中以第一作者在ACM TOG, IEEE TVCG, CGF上发表论文十余篇。EGSR 2021, HPG 2021程序委员会委员。
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