10 – 12 July 2019
Organized by
EUROGRAPHICS THEEUROPEANASSOCIATION
FORCOMPUTERGRAPHICS
Program Co-Chairs
Tamy Boubekeur, LTCI, Telecom Paris, Institut Polytechnique de Paris, France Pradeep Sen, UCSB MIRAGE Lab, University of California, Santa Barbara, USA
Local Organizing Committee
Rémi Allègre: ICube, Université de Strasbourg, CNRS, France Jean-Michel Dischler: ICube, Université de Strasbourg, France Basile Sauvage: ICube, Université de Strasbourg, CNRS, France
DOI: 10.1111/cgf.13784 https://www.eg.org https://diglib.eg.org
International Programme Committee
Miika Aittala, MIT, USA Pascal Barla, INRIA, France Laurent Belcour, Unity, France
Chun-Fa Chang, National Taiwan Normal University, Taiwan Per Christensen, Pixar, USA
Carsten Dachsbacher, Karlsruhe Institute of Technology, Germany George Drettakis, INRIA, France
Philip Dutré, KU Leuven, Belgium Elmar Eisemann, TU Delft, Netherlands Luca Fascione, Weta, New Zealand Elena Garces, Technicolor, France Iliyan Georgiev, Solid Angle, UK Abhijeet Ghosh, Imperial College, UK Toshiya Hachisuka, Tokyo University, Japan Miloš Hašan, Adobe, USA
Wenzel Jakob, EPFL, Switzerland
Adrián Jarabo, University of Zaragoza, Spain Wojciech Jarosz, Dartmouth, USA
Hendrik Lensch, Tübingen University, Germany Marcus Magnor, TU Braunschweig, Germany Steve Marschner, Cornell, USA
Bochang Moon, GIST, South Korea Diego Nehab, IMPA, Brazil
Derek Nowrouzezahrai, McGill, Canada
Marta Ortín Obón, University of Zaragoza, Spain Mathias Paulin, Université Toulouse, France Pieter Peers, College of William & Mary, USA Tobias Ritschel, University College London, UK Fabrice Rousselle, NVIDIA, USA
Holly Rushmeier, Yale University, USA
Philipp Slusallek, Saarland University, Germany Xin Sun, Adobe Research, USA
Min Tang, Zhejiang University, China Xin Tong, Microsoft Research Asia, China Rui Wang , University of Amherst, USA Li-Yi Wei, Univ. of Hong Kong, China
Alexander Wilkie, Charles University, Czech Republic Michael Wimmer, TU Wien, Austria
Hongzhi Wu, Zhejiang University, China Lingqi Yan, UC Santa Barbara, USA Lei Yang, NVIDIA, USA
Sung-Eui Yoon, KAIST, South Korea Cem Yuksel, University of Utah, USA Shuang Zhao, UC Irvine, USA
Kun Zhou, Zhejiang University, China
Author Index
Aittala, Miika . . . 1
Belcour, Laurent . . . 149
Bittner, Jiˇrí . . . 49
Bousseau, Adrien . . . 1
Celarek, Adam . . . 111
Chermain, Xavier . . . 27
Christensen, Per . . . 135
Claux, Frédéric . . . 27
Dachsbacher, Carsten . . . 87
Deschaintre, Valentin . . . 1
Desrichard, François . . . 183
Drettakis, George . . . 1
Durand, Fredo . . . 1
Enayet, Afnan . . . 135
Engel, Klaus . . . 67
Ghosh, Abhijeet . . . 15
Golla, Tim . . . 77
Grosch, Thorsten . . . 39
Gruson, Adrien . . . 57
Hachisuka, Toshiya . . . 57
Hanika, Johannes . . . 87
Hartnagel, Stefan . . . 67
Heitz, Eric . . . 149
Hendrich, Jakub . . . 49
Hermann, Max . . . 193
Hermosilla, Pedro . . . 207
Hladky, Jozef . . . 171
Jakob, Wenzel . . . 87, 111 Jarosz, Wojciech . . . 57, 135, 159 Jendersie, Johannes . . . 39
Jung, Alisa . . . 87
Keinert, Benjamin . . . 67
Kensler, Andrew . . . 135
Kilpatrick, Charlie . . . 135
Klein, Reinhard . . . 77, 193 Kneiphof, Tom . . . 77
Lehtinen, Jaakko . . . 111
Lin, Yiming . . . 15
Liu, Yifan . . . 123
Maisch, Sebastian . . . 207
Martschinke, Jana . . . 67
Meister, Daniel . . . 49
Mérillou, Stéphane . . . 27
Merzbach, Sebastian . . . 193
Mortensen, Troels . . . 57
Nowrouzezahrai, Derek . . . 57
Paulin, Mathias . . . 183
Peers, Pieter . . . 15
Pospíšil, Adam . . . 49
Ritschel, Tobias . . . 207
Ropinski, Timo . . . 207
Rump, Martin . . . 193
Salesin, Katherine . . . 159
Seidel, Hans-Peter . . . 171
Stamminger, Marc . . . 67
Steinberg, Shlomi . . . 97
Steinberger, Markus . . . 171
Stokholm, Heine . . . 57
Vanderhaeghe, David . . . 183
Vibert, Nicolas . . . 57
Wilkie, Alexander . . . 87
Wimmer, Michael . . . 111
Xu, Kun . . . 123
Yan, Ling-Qi . . . 123
TABLE OF CONTENTS
Materials and Reflectance
Flexible SVBRDF Capture with a Multi-Image Deep Network
Valentin Deschaintre, Miika Aittala, Fredo Durand, George Drettakis, and Adrien Bousseau
1
On-Site Example-Based Material Appearance Acquisition Yiming Lin, Pieter Peers, and Abhijeet Ghosh
15
Glint Rendering based on a Multiple-Scattering Patch BRDF Xavier Chermain, Frédéric Claux, and Stéphane Mérillou
27
Microfacet Model Regularization for Robust Light Transport Johannes Jendersie and Thorsten Grosch
39
High Performance Rendering
Ray Classification for Accelerated BVH Traversal
Jakub Hendrich, Adam Pospíšil, Daniel Meister, and Jiˇrí Bittner
49
Scalable Virtual Ray Lights Rendering for Participating Media
Nicolas Vibert, Adrien Gruson, Heine Stokholm, Troels Mortensen, Wojciech Jarosz, Toshiya Hachisuka, and Derek Nowrouzezahrai
57
Adaptive Temporal Sampling for Volumetric Path Tracing of Medical Data
Jana Martschinke, Stefan Hartnagel, Benjamin Keinert, Klaus Engel, and Marc Stamminger
67
Spectral Effects
Real-time Image-based Lighting of Microfacet BRDFs with Varying Iridescence Tom Kneiphof, Tim Golla, and Reinhard Klein
77
Wide Gamut Spectral Upsampling with Fluorescence
Alisa Jung, Alexander Wilkie, Johannes Hanika, Wenzel Jakob, and Carsten Dachsbacher
87
Analytic Spectral Integration of Birefringence-Induced Iridescence Shlomi Steinberg
97
Light Transport
Quantifying the Error of Light Transport Algorithms
Adam Celarek, Wenzel Jakob, Michael Wimmer, and Jaakko Lehtinen
111
Adaptive BRDF-Oriented Multiple Importance Sampling of Many Lights Yifan Liu, Kun Xu, and Ling-Qi Yan
123
Sampling
Orthogonal Array Sampling for Monte Carlo Rendering
Wojciech Jarosz, Afnan Enayet, Andrew Kensler, Charlie Kilpatrick, and Per Christensen
135
Distributing Monte Carlo Errors as a Blue Noise in Screen Space by Permuting Pixel Seeds Between Frames
Eric Heitz and Laurent Belcour
149
Combining Point and Line Samples for Direct Illumination Katherine Salesin and Wojciech Jarosz
159
TABLE OF CONTENTS
Interactive and Real-time Rendering Tessellated Shading Streaming
Jozef Hladky, Hans-Peter Seidel, and Markus Steinberger
171
Global Illumination Shadow Layers
François Desrichard, David Vanderhaeghe, and Mathias Paulin
183
Deep Learning
Learned Fitting of Spatially Varying BRDFs
Sebastian Merzbach, Max Hermann, Martin Rump, and Reinhard Klein
193
Deep-learning the Latent Space of Light Transport
Pedro Hermosilla, Sebastian Maisch, Tobias Ritschel, and Timo Ropinski
207
Keynote
Why Learn Something you Already Know?
Jaakko Lehtinen
Aalto University & NVIDIA Abstract
While computer graphics has many faces, a central one is the fact that it enables creation of photorealistic pictures by simulating light propagation, motion, shape, appearance, and so on. In this talk, I’ll argue that this ability puts graphics research in a unique position to make fundamental contributions to machine learning and AI, while solving its own longstanding problems.
The majority of modern high-performing machine learning models are not particularly interpretable; you cannot, say, interrogate an image-generating Generative Adversarial Network (GAN) to truly tease apart shape, appearance, lighting, and motion, or directly instruct an image classifier to pay attention to shape instead of texture. Yet, reason- ing in such terms is the bread and butter of graphics algorithms! I argue that tightly combining the power of modern machine learning models with sophisticated graphics simulators will enable us to push the learning beyond pixels, into the physically meaningful, interpretable constituents of the world that are all tied together by the fact they come together under well-understood physical processes to form pictures. Of course, such “simulator-based inference” or
“analysis by synthesis” is seeing an increasing interest in the research community, but I’ll try to convince you that what we’re seeing at the moment is just a small sample of things to come.
Short Biography
Jaakko Lehtinen is a tenured associate professor at Aalto University, and a research scientist at NVIDIA Research.
Prior to that, he spent a few years as a postdoc with Frédo Durand at MIT. He works on computer graphics and computer vision, in particular realistic image synthesis, appearance acquisition, and procedural animation.
Keynote
Neural Scene Representation and Rendering
Ali Eslami
Google DeepMind Abstract
In this talk I will introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors, and to render those scenes from any new viewpoint. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this rep- resentation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning and rendering without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand and imagine the world around them.
Short Biography
S. M. Ali Eslami is a staff research scientist at DeepMind. His research is focused on getting computers to learn generative models of images that not only produce good samples but also good explanations for their observations.
Prior to this, he was a post-doctoral researcher at Microsoft Research in Cambridge. He did his PhD in the School of Informatics at the University of Edinburgh, during which he was also a visiting researcher in the Visual Geometry Group at the University of Oxford.
