Eurographics 2017 Workshop on 3D Object Retrieval
Lyon, France April 23 – 24, 2017
Workshop Chair
Guillaume Lavoué, University of Lyon, France
Programme Chairs
Ioannis Pratikakis, Democritus University of Thrace, Greece Florent Dupont, University of Lyon, France
Maks Ovsjanikov, LIX - Ecole polytechnique, France
Proceedings Production Editor
Dieter Fellner (TU Darmstadt & Fraunhofer IGD, Germany) Sponsored by EUROGRAPHICS Association
DOI: 10.2312/3dor.20172005
Dieter W. Fellner, Werner Hansmann, Werner Purgathofer, François Sillion Series Editors
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Copyright c2017 by the Eurographics Association Postfach 2926, 38629 Goslar, Germany
Published by the Eurographics Association –Postfach 2926, 38629 Goslar, Germany–
in cooperation with
Institute of Computer Graphics & Knowledge Visualization at Graz University of Technology and
Fraunhofer IGD (Fraunhofer Institute for Computer Graphics Research), Darmstadt ISBN 978-3-03868-030-7
ISSN 1997-0471 (online)
The electronic version of the proceedings is available from the Eurographics Digital Library at http://diglib.eg.org
Table of Contents
Table of Contents . . . iii
Co-Organizers and Supporter . . . v
International Programme Committee . . . vi
Author Index . . . vii
Keynote . . . ix
Paper Session I Exploiting the PANORAMA Representation for Convolutional Neural Network Classification and Retrieval . . . . 1
Konstantinos Sfikas, Theoharis Theoharis, and Ioannis Pratikakis LightNet: A Lightweight 3D Convolutional Neural Network for Real-Time 3D Object Recognition . . . 9
Shuaifeng Zhi, Yongxiang Liu, Xiang Li, and Yulan Guo Unstructured Point Cloud Semantic Labeling Using Deep Segmentation Networks . . . 17
Alexandre Boulch, Bertrand Le Saux, and Nicolas Audebert SHREC Session I RGB-D to CAD Retrieval with ObjectNN Dataset . . . 25
Binh-Son Hua, Quang-Trung Truong, Minh-Khoi Tran, Quang-Hieu Pham, Asako Kanezaki, Tang Lee, HungYueh Chiang, Winston Hsu, Bo Li, Yijuan Lu, Henry Johan, Shoki Tashiro, Masaki Aono, Minh-Triet Tran, Viet-Khoi Pham, Hai-Dang Nguyen, Vinh-Tiep Nguyen, Quang-Thang Tran, Thuyen V. Phan, Bao Truong, Minh N. Do, Anh-Duc Duong, Lap-Fai Yu, Duc Thanh Nguyen, and Sai-Kit Yeung 3D Hand Gesture Recognition Using a Depth and Skeletal Dataset . . . 33
Quentin De Smedt, Hazem Wannous, Jean-Philippe Vandeborre, J. Guerry, B. Le Saux, and D. Filliat Large-Scale 3D Shape Retrieval from ShapeNet Core55 . . . 39
Manolis Savva, Fisher Yu, Hao Su, Asako Kanezaki, Takahiko Furuya, Ryutarou Ohbuchi, Zhichao Zhou, Rui Yu, Song Bai, Xiang Bai, Masaki Aono, Atsushi Tatsuma, S. Thermos, A. Axenopoulos, G. Th. Papadopoulos, P. Daras, Xiao Deng, Zhouhui Lian, Bo Li, Henry Johan, Yijuan Lu, and Sanjeev Mk Posters Shape Similarity System driven by Digital Elevation Models for Non-rigid Shape Retrieval . . . 51
Daniela Craciun, Guillaume Levieux, and Matthieu Montes Sketch-based 3D Object Retrieval with Skeleton Line Views - Initial Results and Research Problems . . . 55
Xueqing Zhao, Robert Gregor, Pavlos Mavridis, and Tobias Schreck GSHOT: a Global Descriptor from SHOT to Reduce Time and Space Requirements . . . 59 Carlos M. Mateo, Pablo Gil, and Fernando Torres
Table of Contents
A Framework Based on Compressed Manifold Modes for Robust Local Spectral Analysis . . . 63 Sylvain Haas, Atilla Baskurt, Florent Dupont, and Florence Denis
SHREC Session II
Protein Shape Retrieval . . . 67 Na Song, Daniela Craciun, Charles W. Christoffer, Xusi Han, Daisuke Kihara, Guillaume Levieux,
Matthieu Montes, Hong Qin, Pranjal Sahu, Genki Terashi, and Haiguang Liu
Point-Cloud Shape Retrieval of Non-Rigid Toys . . . 75 F. A. Limberger, R. C. Wilson, M. Aono, N. Audebert, A. Boulch, B. Bustos, A. Giachetti, A. Godil,
B. Le Saux, B. Li, Y. Lu, H.-D. Nguyen, V.-T. Nguyen, V.-K. Pham, I. Sipiran, A. Tatsuma, M.-T. Tran, and S. Velasco-Forero
Deformable Shape Retrieval with Missing Parts . . . 85 E. Rodolà, L. Cosmo, O. Litany, M. M. Bronstein, A. M. Bronstein, N. Audebert, A. Ben Hamza,
A. Boulch, U. Castellani, M. N. Do, A.-D. Duong, T. Furuya, A. Gasparetto, Y. Hong, J. Kim, B. Le Saux,
R. Litman, M. Masoumi, G. Minello, H.-D. Nguyen, V.-T. Nguyen, R. Ohbuchi, V.-K. Pham, T. V. Phan, M. Rezaei, A. Torsello, M.-T. Tran, Q.-T. Tran, B. Truong, L. Wan, and C. Zou
Retrieval of Surfaces with Similar Relief Patterns . . . 95 S. Biasotti, E. Moscoso Thompson, M. Aono, A. Ben Hamza, B. Bustos, S. Dong, B. Du, A. Fehri,
H. Li, F. A. Limberger, M. Masoumi, M. Rezaei, I. Sipiran, L. Sun, A. Tatsuma, S. Velasco Forero, R. C. Wilson, Y. Wu, J. Zhang, T. Zhao, F. Fornasa, and A. Giachetti
Paper Session II
3D Mesh Unfolding via Semidefinite Programming . . . 105 Juncheng Liu, Zhouhui Lian, and Jianguo Xiao
Directed Curvature Histograms for Robotic Grasping . . . 113 Rodrigo Schulz, Pablo Guerrero, and Benjamin Bustos
Semantic Correspondence Across 3D Models for Example-based Modeling . . . 121 Vincent Léon, Vincent Itier, Nicolas Bonneel, Guillaume Lavoué, and Jean-Philippe Vandeborre
Towards Recognizing of 3D Models Using A Single Image . . . 129 Hatem A. Rashwan, Sylvie Chambon, Geraldine Morin, Pierre Gurdjos, and Vincent Charvillat
Co-Organizers and Supporter
International Programme Committee Yiannis Aloimonos (UMIACS, USA)
Halim Benhabiles (University of Blida, Algeria) Stefano Berretti (University of Florence, Italy) Silvia Biasotti (IMATI-CNR Genoa, Italy)
Michael Bronstein (Universita’ della Svizzera Italiana, Switzerland) Benjamin Bustos (University of Chile, Chile)
Umberto Castellani (University of Verona, Italy)
Mohamed Daoudi (Télécom Lille 1 / Institut Mines-Télécom, France) Petros Daras (Informatics and Telematics Institute, Greece)
Alberto Del Bimbo (University of Florence, Italy) Bianca Falcidieno (IMATI-CNR, Italy)
Alfredo Ferreira (Technical University of Lisbon, Portugal) Andrea Giachetti (University of Verona, Italy)
Daniela Giorgi (ISTI-CNR, Italy)
Afzal Godil (National Institute of the Standards and Technology, USA) A. Ben Hamza (Concordia University, Montreal, Canada)
Oliver van Kaick (Simon Fraser University, Canada) Ron Kimmel (Technion, Israel)
Zhouhui Lian (Peking University, Beijing, China) Ryutarou Ohbuchi (University of Yamanashi, Japan) Georgios Papaioannou (AUEB, Greece)
David Picard (ETIS-ENSEA, France)
Herindrasana Ramampiaro (NTNU, Norway) Raif M. Rustamov (Stanford University, USA)
Nickolas S. Sapidis (University of Western Macedonia, Greece) Tobias Schreck (Graz University of Technology, Germany) Ivan Sipiran (Pontificia Universidad Católica del Perú) Michela Spagnuolo (IMATI-CNR, Italy)
Hedi Tabia (ETIS-ENSEA, France)
Theoharis Theoharis (Norwegian University of Science and Technology, Norway) Jean-Philippe Vandeborre (Télécom Lille / Institut Mines-Télécom, LIFL, France) Remco Veltkamp (University of Utrecht, Netherlands)
Hazem Wannous (University Lille1 / LIFL, France)
Kevin (Kai) Xu (National University of Defense Technology, China)
Author Index Aono, Masaki . . . 25, 39, 75, 95
Audebert, Nicolas . . . 17, 75, 85
Axenopoulos, A. . . 39
Bai, Song . . . 39
Bai, Xiang . . . 39
Baskurt, Atilla . . . 63
Biasotti, S. . . 95
Bonneel, Nicolas . . . 121
Boulch, Alexandre . . . 17, 75, 85 Bronstein, A. M. . . 85
Bronstein, M. M. . . 85
Bustos, Benjamin . . . 75, 95, 113 Castellani, U. . . 85
Chambon, Sylvie . . . 129
Charvillat, Vincent . . . 129
Chiang, HungYueh . . . 25
Christoffer, Charles W. . . 67
Cosmo, L. . . 85
Craciun, Daniela . . . 51, 67 Daras, P. . . 39
Deng, Xiao . . . 39
Denis, Florence . . . 63
Do, Minh N. . . 25, 85 Dong, S. . . 95
Du, B. . . 95
Duong, Anh-Duc . . . 25, 85 Dupont, Florent . . . 63
Fehri, A. . . 95
Filliat, D. . . 33
Forero, S. Velasco . . . 95
Fornasa, F. . . 95
Furuya, Takahiko . . . 39, 85 Gasparetto, A. . . 85
Giachetti, A. . . 75, 95 Gil, Pablo . . . 59
Godil, A. . . 75
Gregor, Robert . . . 55
Guerrero, Pablo . . . 113
Guerry, J. . . 33
Guo, Yulan . . . 9
Gurdjos, Pierre . . . 129
Haas, Sylvain . . . 63
Hamza, A. Ben . . . 85, 95 Han, Xusi . . . 67
Hong, Y. . . 85
Hsu, Winston . . . 25
Hua, Binh-Son . . . 25
Itier, Vincent . . . 121
Johan, Henry . . . 25, 39 Kanezaki, Asako . . . 25, 39 Kihara, Daisuke . . . 67
Kim, J. . . 85
Lavoué, Guillaume . . . 121
Lee, Tang . . . 25
Léon, Vincent . . . 121
Levieux, Guillaume . . . 51, 67 Li, B. . . 75
Li, Bo . . . 25, 39 Li, H. . . 95
Li, Xiang . . . 9
Lian, Zhouhui . . . 39, 105 Limberger, F. A. . . 75, 95 Litany, O. . . 85
Litman, R. . . 85
Liu, Haiguang . . . 67
Liu, Juncheng . . . 105
Liu, Yongxiang . . . 9
Lu, Y. . . 75
Lu, Yijuan . . . 25, 39 Masoumi, M. . . 85, 95 Mateo, Carlos M. . . 59
Mavridis, Pavlos . . . 55
Minello, G. . . 85
Mk, Sanjeev . . . 39
Montes, Matthieu . . . 51, 67 Morin, Geraldine . . . 129
Nguyen, Duc Thanh . . . 25
Nguyen, Hai-Dang . . . 25, 75, 85 Nguyen, Vinh-Tiep . . . 25, 75, 85 Ohbuchi, Ryutarou . . . 39, 85 Papadopoulos, G. Th. . . 39
Pham, Quang-Hieu . . . 25
Pham, Viet-Khoi . . . 25, 75, 85 Phan, Thuyen V. . . 25, 85 Pratikakis, Ioannis . . . 1
Qin, Hong . . . 67
Author Index
Rashwan, Hatem A. . . 129
Rezaei, M. . . 85, 95 Rodolà, E. . . 85
Sahu, Pranjal . . . 67
Saux, Bertrand Le . . . 17, 33, 75, 85 Savva, Manolis . . . 39
Schreck, Tobias . . . 55
Schulz, Rodrigo . . . 113
Sfikas, Konstantinos . . . 1
Sipiran, I. . . 75, 95 Smedt, Quentin De . . . 33
Song, Na . . . 67
Su, Hao . . . 39
Sun, L. . . 95
Tashiro, Shoki . . . 25
Tatsuma, Atsushi . . . 39, 75, 95 Terashi, Genki . . . 67
Theoharis, Theoharis . . . 1
Thermos, S. . . 39
Thompson, E. Moscoso . . . 95
Torres, Fernando . . . 59
Torsello, A. . . 85
Tran, Minh-Khoi . . . 25
Tran, Minh-Triet . . . 25, 75, 85 Tran, Quang-Thang . . . 25, 85 Truong, Bao . . . 25, 85 Truong, Quang-Trung . . . 25
Vandeborre, Jean-Philippe . . . 33, 121 Velasco-Forero, S. . . 75
Wan, L. . . 85
Wannous, Hazem . . . 33
Wilson, R. C. . . 75, 95 Wu, Y. . . 95
Xiao, Jianguo . . . 105
Yeung, Sai-Kit . . . 25
Yu, Fisher . . . 39
Yu, Lap-Fai . . . 25
Yu, Rui . . . 39
Zhang, J. . . 95
Zhao, T. . . 95
Zhao, Xueqing . . . 55
Zhi, Shuaifeng . . . 9
Zhou, Zhichao . . . 39
Zou, C. . . 85
Keynote Object-Centric Machine Learning
Leonidas Guibas Stanford University Abstract
Deep knowledge of the world is necessary if we are to have autonomous and intelligent agents and artifacts that can assist us or even carry out tasks entirely independently. One way to factorize the complexity of the world is to associate information and knowledge with stable entities, animate or inanimate, such as a per- son or a vehicle, etc. In this talk I’ll survey a number of recent efforts whose aim is to create and annotate reference representations for objects based on 3D models with the aim of delivering such information to new observations, as needed. In this object-centric view, the goal is to use these reference representations for aggregating information and knowledge about object geometry, appearance, articulation, materials, physical properties, affordances, and functionality. We acquire such information in a multitude of ways, both from crowd-sourcing and from establishing direct links between models and signals, such as images, videos, and 3D scans – and through these to language and text. The purity of the 3D representation al- lows us to establish robust maps and correspondences for transferring information among the 3D models themselves – making our current 3D repository, ShapeNet, a true network. Information transport and ag- gregation in such networks naturally lead to abstractions of objects and other visual entities, allowing data compression while capturing variability as well as shared structure. Furthermore, the network can act as a regularizer, allowing us to to benefit from the “wisdom of the collection” in performing operations on indi- vidual data sets or in map inference between them. This effectively enables us to add missing information to signals through computational imagination, giving us for example the ability to infer what an occluded part of an object in an image may look like, or what other object arrangements may be possible, based on the world-knowledge encoded in ShapeNet and other repositories. I will also briefly discuss current approaches in designing deep neural network architectures appropriate for operating directly on irregular 3D data, as well as ways to learn object function from observing multiple action sequences involving objects.
Short Biography
Leonidas Guibas obtained his Ph.D. from Stanford University under the supervision of Donald Knuth. His main subsequent employers were Xerox PARC, DEC/SRC, MIT, and Stanford. He is currently the Paul Pigott Professor of Computer Science (and by courtesy, Electrical Engineering) at Stanford University. He heads the Geometric Computation group and is part of the AI Laboratory, the Graphics Laboratory, the Bio-X Program, and the Institute for Computational and Mathematical Engineering. Professor Guibas’
interests span geometric data analysis, computational geometry, geometric modeling, computer graphics, computer vision, robotics, ad hoc communication and sensor networks, and discrete algorithms. Some well-known past accomplishments include the analysis of double hashing, red-black trees, the quad-edge data structure, Voronoi-Delaunay algorithms, the Earth Mover’s distance, Kinetic Data Structures (KDS), Metropolis light transport, heat-kernel signatures, and functional maps. Professor Guibas is a member of the National Academy of Engineering, an ACM Fellow, an IEEE Fellow and winner of the ACM Allen Newell award and the ICCV Helmholtz prize.
Keynote
Some Recent Developments in 3D Shape Acquisition and Analysis
Daniel Cremers
Technical University, Munich Abstract
The reconstruction and understanding of the 3D world from images is among the central challenges in computer vision. Starting in the 2000s, researchers have pioneered algorithms which can reconstruct cam- era motion and sparse feature points in real-time. In my talk, I will introduce spatially dense methods for camera tracking and 3D reconstruction which do not require feature point estimation, which exploit all available input data and which recover dense or semi-dense geometry rather than sparse point clouds. In addition, I will present algorithms to analyze 3D shapes, including the computation of shape descriptors and shape correspondence.
Short Biography
Daniel Cremers obtained a PhD in Computer Science from the University of Mannheim, Germany. Subse- quently he spent two years as a postdoctoral researcher at UCLA and one year as a permanent researcher at Siemens Corporate Research in Princeton, NJ. From 2005 until 2009 he was associate professor at the University of Bonn, Germany. Since 2009 he holds the chair for Computer Vision and Pattern Recogni- tion at the Technical University, Munich. His publications received numerous awards, including the ‘Best Paper of the Year 2003’ (Int. Pattern Recognition Society), the ‘Olympus Award 2004’ (German Soc. for Pattern Recognition) and the ‘2005 UCLA Chancellor’s Award for Postdoctoral Research’. For pioneer- ing research he received a Starting Grant (2009), a Proof of Concept Grant (2014) and a Consolidator Grant (2015) by the European Research Council. In 2010 he was listed among “Germany’s top 40 re- searchers below 40” (Capital). Prof. Cremers received the Gottfried-Wilhelm Leibniz Award 2016, the most important research award in German academia.
