July 12 – 14, 2021
Organized by
EUROGRAPHICS THEEUROPEANASSOCIATION
FORCOMPUTERGRAPHICS
Conference Co-Chairs Angel Chang, Simon Fraser University
Alec Jacobson, University of Toronto
Technical Program Chairs Julie Digne, LIRIS - CNRS
Keenan Crane, Carnegie Mellon University
DOI: 10.1111/cgf.14376 https://www.eg.org https://diglib.eg.org
Sponsors
Huawei
International Programme Committee
Noam Aigerman Adobe Research, US Marc Alexa TU Berlin, DE Pierre Alliez INRIA, FR Marco Attene CNR IMATI, IT Omri Azencot
Ben-Gurion Univeristy, IL Alexander Belyaev Heriot-Watt University, GB Mirela Ben-Chen Technion-IIT, IL Bedrich Benes Purdue University, US Amit Bermano Tel-Aviv University, IL David Bommes University of Bern, CH Mario Botsch TU Dortmund, DE Tamy Boubekeur Adobe 3D&Immersive, FR Marcel Campen Osnabrück University, DE Raphaëlle Chaine Université Lyon, FR Siddhartha Chaudhuri Adobe Research, IN Renjie Chen USTC, CN Edward Chien MIT, US Paolo Cignoni CNR ISTI, IT
David Cohen-Steiner INRIA, FR
Olga Diamanti TU Graz, AT
John Evans
University of Colorado Boulder, US Lin Gao
Florida State University, US Xifeng Gao
Florida State University, US Yotam Gingold
Georges Mason University, US Daniela Giorgi
CNR - ISTI, IT Fernando de Goes Pixar, US
Eitan Grinspun University of Toronto, CA Gaël Guennebaud INRIA, FR
Paul Guerrero Adobe Research, GB Stefanie Hahmann
Université Grenoble-Alpes, FR Rana Hanocka
Tel-Aviv University, IL Su Hao
UC San Diego, US Ying He
Nanyang Technological University, CN Philipp Herholz
ETH Zurich, CH Klaus Hildebrandt TU Delft, NL Kai Hormann
Università della Svizzera italiana, CH Jin Huang
Zhejiang University, CN Alec Jacobson
University of Toronto, CA Tao Ju
Washington University in St. Louis, US Bert Jüttler
Johannes Kepler Universität, AT
International Programme Committee
Misha Kazhdan
Johns Hopkins University, US John Keyser
Texas A&M University, US Myung Soo Kim
Seoul National University, KR Vladimir Kim
Adobe Research, US Leif Kobbelt
RWTH Aachen University, DE Mina Konakovic-Lukovic MIT, US
Jiri Kosinka
University of Groningen, NL Yu-Kun Lai
Cardiff University, GB Manfred Lau
City University of Hong Kong, HK Ligang Liu
University of Science and Technology of China, CN Tiantian Liu
Microsoft Research Asia, CN Yang Liu
Microsoft Research Asia, CN Marco Livesu
CNR IMATI, IT Luigi Malomo CNR ISTI, IT Nicolas Mellado CNRS, FR Pooran Memari CNRS, FR
Przemyslaw Musialski
New Jersey Insitute of Technology, US Julian Panetta
UC Davis, US Daniele Panozzo NYU, US
Giuseppe Patane CNR IMATI, IT
Jorg Peters
University of Florida, US Nico Pietroni
University of Technology Sydney, AU Konrad Polthier
Freie Universität Berlin, DE Roi Poranne
University of Haifa, IL Enrico Puppo University of Genoa, IT Emanuele Rodola
Sapienza University of Rome, IT Martin Rumpf
Bonn University, DE Raif Rustamov AT&T Inc, US Leonardo Sacht
Universidade Federal de Santa Catarina, BR Manolis Savva
Simon Fraser University, CA Scott Schaefer
Texas A&M University, US Ryan Schmidt
Epic Games, CA Teseo Schneider University of Victoria, CA Nicholas Sharp
Carnegie Mellon University, US Justin Solomon
MIT, US
Olga Sorkine-Hornung ETH Zürich, CH
Oded Stein MIT, US
Kenshi Takayama
National Institute of Informatics, JP Chengcheng Tang
Facebook, US Jean-Marc Thiery Télécom ParisTech, FR
International Programme Committee
Bernhard Thomaszewski University of Montreal, CA Amir Vaxman
Utrecht University, NL Etienne Vouga
University of Texas at Austin, US Johannes Wallner
TU Graz, AT Michael Wand University of Mainz, DE Wenping Wang
University of Hong Kong, HK Ofir Weber
Bar-Ilan University, IL
Kai Xu
National University of Defense Technology, CN Yong-Liang Yang
University of Bath, GB Eugene Zhang
Oregon State University, US Hao Zhang
Simon Fraser University, CA Yongjie Zhang
Carnegie Mellon University, US Qingnan Zhou
Adobe Research, US
TABLE OF CONTENTS
Data and Acquisition
A Benchmark Dataset for Repetitive Pattern Recognition on Textured 3D Surfaces Stefan Lengauer, Ivan Sipiran, Reinhold Preiner, Tobias Schreck, and Benjamin Bustos
1
SimJEB: Simulated Jet Engine Bracket Dataset Eamon Whalen, Azariah Beyene, and Caitlin Mueller
9
A Robust Multi-View System for High-Fidelity Human Body Shape Reconstruction
Qitong Zhang, Lei Wang, Linlin Ge, Shan Luo, Taihao Zhu, Feng Jiang, Jimmy Ding, and Jieqing Feng
19
Shape Synthesis and Editing
Gauss Stylization: Interactive Artistic Mesh Modeling based on Preferred Surface Normals Maximilian Kohlbrenner, Ugo Finnendahl, Tobias Djuren, and Marc Alexa
33
Normal-Driven Spherical Shape Analogies Hsueh-Ti Derek Liu and Alec Jacobson
45
Roominoes: Generating Novel 3D Floor Plans From Existing 3D Rooms
Kai Wang, Xianghao Xu, Leon Lei, Selena Ling, Natalie Lindsay, Angel Xuan Chang, Mano- lis Savva, and Daniel Ritchie
57
Blending of Hyperbolic Closed Curves Aziz Ikemakhen and T. Ahanchaou
71
Shape Matching
Discrete Optimization for Shape Matching
Jing Ren, Simone Melzi, Peter Wonka, and Maks Ovsjanikov
81
A Data-Driven Approach to Functional Map Construction and Bases Pursuit Omri Azencot and Rongjie Lai
97
Globally Injective Geometry Optimization with Non-Injective Steps Matthew Overby, Danny Kaufman, and Rahul Narain
111
Surface Reconstruction
Delaunay Meshing and Repairing of NURBS Models Xiao Xiao, Pierre Alliez, Laurent Busé, and Laurent Rineau
125
Progressive Discrete Domains for Implicit Surface Reconstruction
Tong Zhao, Pierre Alliez, Tamy Boubekeur, Laurent Busé, and Jean-Marc Thiery
143
Scalable Surface Reconstruction with Delaunay-Graph Neural Networks Raphael Sulzer, Loic Landrieu, Renaud Marlet, and Bruno Vallet
157
Direction Fields and Quads
Simpler Quad Layouts using Relaxed Singularities Max Lyon, Marcel Campen, and Leif Kobbelt
169
Learning Direction Fields for Quad Mesh Generation Alexander Dielen, Isaak Lim, Max Lyon, and Leif Kobbelt
181
TABLE OF CONTENTS
Surface Map Homology Inference
Janis Born, Patrick Schmidt, Marcel Campen, and Leif Kobbelt
193
Differential Operators
Stable and Efficient Differential Estimators on Oriented Point Clouds Thibault Lejemble, David Coeurjolly, Loïc Barthe, and Nicolas Mellado
205
The Diamond Laplace for Polygonal and Polyhedral Meshes Astrid Bunge, Mario Botsch, and Marc Alexa
217
Frame Field Operators
David Palmer, Oded Stein, and Justin Solomon
231
Distances
Geodesic Distance Computation via Virtual Source Propagation Philip Trettner, David Bommes, and Leif Kobbelt
247
Practical Computation of the Cut Locus on Discrete Surfaces Claudio Mancinelli, Marco Livesu, and Enrico Puppo
261
On Landmark Distances in Polygons Craig Gotsman and Kai Hormann
275
Fabrication
Developable Approximation via Gauss Image Thinning
Alexandre Binninger, Floor Verhoeven, Philipp Herholz, and Olga Sorkine-Hornung
289
Fabrication-Aware Reverse Engineering for Carpentry
James Noeckel, Haisen Zhao, Brian Curless, and Adriana Schulz
301
Author Index
Ahanchaou, T. . . 71
Alexa, Marc . . . 33, 217 Alliez, Pierre . . . 125, 143 Azencot, Omri . . . 97
Barthe, Loïc . . . 205
Beyene, Azariah . . . 9
Binninger, Alexandre . . . 289
Bommes, David . . . 247
Born, Janis . . . 193
Botsch, Mario . . . 217
Boubekeur, Tamy . . . 143
Bunge, Astrid . . . 217
Busé, Laurent . . . 125, 143 Bustos, Benjamin . . . 1
Campen, Marcel . . . 169, 193 Chang, Angel Xuan . . . 57
Coeurjolly, David . . . 205
Curless, Brian . . . 301
Dielen, Alexander . . . 181
Ding, Jimmy . . . 19
Djuren, Tobias . . . 33
Feng, Jieqing . . . 19
Finnendahl, Ugo . . . 33
Ge, Linlin . . . 19
Gotsman, Craig . . . 275
Herholz, Philipp . . . 289
Hormann, Kai . . . 275
Ikemakhen, Aziz . . . 71
Jacobson, Alec . . . 45
Jiang, Feng . . . 19
Kaufman, Danny . . . 111
Kobbelt, Leif . . . 169, 181, 193, 247 Kohlbrenner, Maximilian . . . 33
Lai, Rongjie . . . 97
Landrieu, Loic . . . 157
Lei, Leon . . . 57
Lejemble, Thibault . . . 205
Lengauer, Stefan . . . 1
Lim, Isaak . . . 181
Lindsay, Natalie . . . 57
Ling, Selena . . . 57
Liu, Hsueh-Ti Derek . . . 45
Livesu, Marco . . . 261
Luo, Shan . . . 19
Lyon, Max . . . 169, 181 Mancinelli, Claudio . . . 261
Marlet, Renaud . . . 157
Mellado, Nicolas . . . 205
Melzi, Simone . . . 81
Mueller, Caitlin . . . 9
Narain, Rahul . . . 111
Noeckel, James . . . 301
Overby, Matthew . . . 111
Ovsjanikov, Maks . . . 81
Palmer, David . . . 231
Preiner, Reinhold . . . 1
Puppo, Enrico . . . 261
Ren, Jing . . . 81
Rineau, Laurent . . . 125
Ritchie, Daniel . . . 57
Savva, Manolis . . . 57
Schmidt, Patrick . . . 193
Schreck, Tobias . . . 1
Schulz, Adriana . . . 301
Sipiran, Ivan . . . 1
Solomon, Justin . . . 231
Sorkine-Hornung, Olga . . . 289
Stein, Oded . . . 231
Sulzer, Raphael . . . 157
Thiery, Jean-Marc . . . 143
Trettner, Philip . . . 247
Vallet, Bruno . . . 157
Verhoeven, Floor . . . 289
Wang, Kai . . . 57
Wang, Lei . . . 19
Whalen, Eamon . . . 9
Wonka, Peter . . . 81
Xiao, Xiao . . . 125
Xu, Xianghao . . . 57
Zhang, Qitong . . . 19
Zhao, Haisen . . . 301
Zhao, Tong . . . 143
Zhu, Taihao . . . 19
Keynote Engineering-driven Design: a new Foundation Bradley Rothenberg
CEO, nTopology
Abstract
nTopology (nTop) solves some of the most difficult problems in shape design, especially those emerging due to additive manufacturing. In doing so, nTop enables new processes for design – it empowers engineers to design parts that are impossible with older software. To achieve these goals, we use some interesting technologies that are new to engineering software – specifically basing our modeling tech on Signed Distance Fields (SDFs). This talk describes the new design problems that engineers today face and the software we have developed for solving them.
Short Biography
Bradley Rothenberg is the founder and CEO of nTopology, an engineering software company based in New York City built to enable engineers to design transformative products. nTopology’s breakthrough technology unifies ge- ometry and physics into finely tuned digital models, supporting engineers as they collaborate to develop lightweight, optimized parts with the requirements built right in. Bradley studied architecture at Pratt Institute in Brooklyn, New York, and has been developing computational design tools for advanced manufacturing for the last 10-plus years.
Keynote
How to Represent Part-whole Hierarchies in a Neural net Geoffrey Hinton
University of Toronto/Google Research
Abstract
I will present a single idea about representation which allows advances made by several different groups to be combined into an imaginary system called GLOM. The advances include transformers, neural fields, contrastive representation learning, distillation and capsules. GLOM answers the question: How can a neural network with a fixed architecture parse an image into a part-whole hierarchy which has a different structure for each image? The idea is simply to use islands of identical vectors to represent the nodes in the parse tree. The talk will discuss the many ramifications of this idea. If GLOM can be made to work, it should significantly improve the interpretability of the representations produced by transformer-like systems when applied to vision or language.
Short Biography
Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. He did postdoctoral work at Sussex University and the University of Califor- nia San Diego and spent five years as a faculty member in the Computer Science department at Carnegie-Mellon University. He then became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto. He spent three years from 1998 until 2001 setting up the Gatsby Computational Neuroscience Unit at University College London and then returned to the University of Toronto where he is now an emeritus distinguished professor. From 2004 until 2013 he was the director of the program on
“Neural Computation and Adaptive Perception” which is funded by the Canadian Institute for Advanced Research.
Since 2013 he has been working half-time for Google in Mountain View and Toronto. Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and the Association for the Advancement of Artificial Intelligence.
He is an honorary foreign member of the American Academy of Arts and Sciences and the National Academy of Engineering, and a former president of the Cognitive Science Society. He has received honorary doctorates from the University of Edinburgh, the University of Sussex, and the University of Sherbrooke. He was awarded the first David E. Rumelhart prize (2001), the IJCAI award for research excellence (2005), the Killam prize for Engineer- ing (2012), The IEEE James Clerk Maxwell Gold medal (2016), and the NSERC Herzberg Gold Medal (2010) which is Canada’s top award in Science and Engineering. Geoffrey Hinton designs machine learning algorithms.
His aim is to discover a learning procedure that is efficient at finding complex structure in large, high-dimensional datasets and to show that this is how the brain learns to see. He was one of the researchers who introduced the back- propagation algorithm and the first to use backpropagation for learning word embeddings. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning, products of experts and deep belief nets. His research group in Toronto made major breakthroughs in deep learning that have revolutionized speech recognition and object classification.
Keynote
Computing Morphing Matter: the Marriage of Geometry and Hidden Forces Lining Yao
Carnegie Mellon University
Abstract
Morphing Matter is an interplay of geometry and hidden forces. Lining Yao, the director of Morphing Matter Lab, will share her team’s experiences of computing, designing, and fabricating morphing mechanisms that leverage both geometrical and physical knowledge of materials. Lining will unfold a few marriages of geometry and forces in the talk: a conformal map interconnecting beams shrinking and fighting for the lowest entropy, a frustum-shaped groove interfering disks swelling with differential diffusion rate, and a triangulated filler path affecting spacer fabric deforming with biased shear forces. Novel morphing mechanisms and applications also come from these marriages, such as self-assembling furniture, crawling soft robots, and morphing pasta.
Short Biography
Lining Yao is an Assistant Professor of Human-Computer Interaction Institute at Carnegie Mellon University (CMU), School of Computer Science, directing the Morphing Matter Lab. Lining also holds courtesy appoint- ments at Mechanical Engineering and Material Sciences and Engineering at CMU. Morphing Matter lab develops processes, materials, tools, and applications of adaptive, dynamic, and intelligent morphing matter from nano to macro scales. Research often combines material science, computational fabrication, and creative design practices.
The mission is to advance both science and society with the design of morphing matter. Lining and her lab work anti-disciplinarily, publishing and exhibiting across science, engineering, design, and art. Lining gained her Ph.D.
from the MIT Media Lab. She is a Wired UK fellow, CMU Provost’s Inclusive Teaching Fellow, and a recipient of the NSF CAREER Award.
