GAMES Webinar 2019 – 125期（计算成像专题）|施柏鑫(北京大学),康凯彰 (浙江大学)
【GAMES Webinar 2020-125期】（计算成像专题）
报告题目：Data-driven Photometric 3D Modeling for Complex Refletances
Modern 3D computer vision methods, represented by multi-view stereo and structure-from-motion, have achieved faithful 3D reconstruction from a set of images. But are the reconstruction quality and density really sufficient for your purpose? Despite requiring more controlled setups than multi-view stereo, photometric approaches have proven to be invaluable tools in applications such as Hollywood movies, industrial quality inspection, and so on, since they can reconstruct fine surface details at superior quality. This talk will mainly cover photometric stereo techniques that take as input a set of images observed under different illumination conditions from a fixed viewpoint to compute the shape in the form of surface normals with the same high resolution as the 2D image. While conventional photometric stereo methods make various assumptions over reflectance and illumination, they are being relaxed in modern methods by powerful machine learning approaches so as to be practical in diverse scenarios, such as objects with complex reflectances. In addition, newly rendered datasets and captured real world datasets have been proposed for training and testing data-driven approaches for photometric stereo, which shows superior performance over non-learning approaches.
Boxin Shi received the BE degree from the Beijing University of Posts and Telecommunications, the ME degree from Peking University, and the PhD degree from the University of Tokyo, in 2007, 2010, and 2013. He is currently a Boya Young Fellow Assistant Professor and Research Professor at Peking University, where he leads the Camera Intelligence Group. Before joining PKU, he did postdoctoral research with MIT Media Lab, Singapore University of Technology and Design, Nanyang Technological University from 2013 to 2016, and worked as a researcher in the National Institute of Advanced Industrial Science and Technology from 2016 to 2017. He won the Best Paper Runner-up award at International Conference on Computational Photography 2015. He served/is serving as an Area Chair for ACCV 2018, BMVC 2019, 3DV 2019, and an Associate Editor for IET Computer Vision.
报告题目：Learning Efficient Illumination Multiplexing for Joint Capture of Reflectance and Shape
We propose a novel framework that automatically learns the lighting patterns for efficient, joint acquisition of unknown reflectance and shape. The core of our framework is a deep neural network, with a shared linear encoder that directly corresponds to the lighting patterns used in physical acquisition, as well as non-linear decoders that output per-pixel normal and diffuse / specular information from photographs. We exploit the diffuse and normal information from multiple views to reconstruct a detailed 3D shape, and then fit BRDF parameters to the diffuse / specular information, producing texture maps as reflectance results. We demonstrate the effectiveness of the framework with physical objects that vary considerably in reflectance and shape, acquired with as few as 16~32 lighting patterns that correspond to 7~15 seconds of per-view acquisition time. Our framework is useful for optimizing the efficiency in both novel and existing setups, as it can automatically adapt to various factors, including the geometry / the lighting layout of the device and the properties of appearance.
I entered Mixed Honors Class in Chu Kochen Honors College of Zhejiang University in 2014. I received B.Eng. degree from College of Computer Science & Technology, Zhejiang University and Honors Degree from Chu Kochen Honors College in 2018. Currently I am a Ph.D. student in the State Key Lab of CAD&CG, Zhejiang University (supervised by Prof.Hongzhi Wu). My research interests include appearance acquisition/modeling and rendering.
陈雪锦，中国科学技术大学副教授。分别于2003年、2008年获得中国科学技术大学学士学位、博士学位。2008年至2010年于耶鲁大学计算机系从事博士后研究，专业为计算机图形学。2010年8月加入中国科学技术大学。2017年2月-8月在斯坦福大学计算机系任访问副教授。主要研究方向为计算机图形学、计算机视觉、多媒体内容分析和理解等。在ACM Trans. Graph., IEEE TVCG, ACM Siggraph asia, Eurographics等国际学术期刊、会议上发表论文40余篇。
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