Publications
Not up to date, please refer to: Google Scholar, DBLP.
Journals and international peer-reviewed conferences:
C43. Isospectralization, or how to hear shape, style, and correspondence
Luca Cosmo, Mikhail Panine, Arianna Rampini, Maks Ovsjanikov, Michael Bronstein, and Emanuele Rodolà.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019).
C42. Unsupervised learning of dense shape correspondence
Oshri Halimi, Or Litany, Emanuele Rodolà, Alex Bronstein, and Ron Kimmel.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019) - Oral Presentation
paper | supplementary | code
C41. GFrames: Gradient-based local reference frame for 3D shape matching
Simone Melzi, Riccardo Spezialetti, Federico Tombari, Michael Bronstein, Luigi Di Stefano, and Emanuele Rodolà.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019) - Oral Presentation
J17. Functional maps representation on product manifolds
Emanuele Rodolà, Zorah Lähner, Alex Bronstein, Michael Bronstein, and Justin Solomon .
Computer Graphics Forum (CGF)
J16. Improved functional mappings via product preservation
Dorian Nogneng, Simone Melzi, Emanuele Rodolà, Umberto Castellani, Michael Bronstein, and Maks Ovsjanikov .
Computer Graphics Forum (CGF) - Proc. EUROGRAPHICS 2018
J15. Localized manifold harmonics for spectral shape analysis
Simone Melzi, Emanuele Rodolà, Umberto Castellani, and Michael Bronstein .
Computer Graphics Forum (CGF) - presented at EUROGRAPHICS 2018
paper | code | supplemetary
C40. Deep Functional Maps: Structured Prediction for Dense Shape Correspondence
Or Litany, Tal Remez, Emanuele Rodolà, Alex Bronstein, and Michael Bronstein.
IEEE International Conference on Computer Vision (ICCV 2017).
C39. Efficient deformable shape correspondence via kernel matching
Matthias Vestner, Zorah L ähner, Amit Boyarski, Or Litany, Ron Slossberg, Tal Remez, Emanuele Rodolà, Alex Bronstein, Michael Bronstein, Ron Kimmel, and Daniel Cremers .
Intl. Conference on 3D Vision (3DV 2017).
C38. Spatial Maps: From low rank spectral to sparse spatial functional representations
Andrea Gasparetto, Luca Cosmo, Emanuele Rodolà, Andrea Torsello, and Michael Bronstein.
Intl. Conference on 3D Vision (3DV 2017).
C37. Computing and Processing Correspondences with Functional Maps
Maks Ovsjanikov, Etienne Corman, Michael M. Bronstein, Emanuele Rodolà, Mirela Ben-Chen, Leonidas Guibas, Frederic Chazal, and Alex M. Bronstein.
Proc. SIGGRAPH Courses (SIGGRAPH 2017).
J14. Regularized Point-wise Map Recovery from Functional Correspondence
Emanuele Rodolà, Michael Moeller, and Daniel Cremers.
Computer Graphics Forum (CGF), 2017
C36. Product Manifold Filter: Non-rigid shape correspondence via kernel density estimation in the product space
Matthias Vestner, Roee Litman, Emanuele Rodolà, Alex Bronstein, and Daniel Cremers.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017).
C35. Geometric deep learning on graphs and manifolds using mixture model CNNs
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, and Michael M. Bronstein.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) - Oral Presentation
C34. SHREC'17: Deformable Shape Retrieval with Missing Parts
Emanuele Rodolà, Luca Cosmo, Or Litany, Michael M. Bronstein, Alex M. Bronstein et al.
EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR 2017).
paper | slides | baseline code | dataset
J13. Fully Spectral Partial Shape Matching
Or Litany, Emanuele Rodolà, Alex M. Bronstein, and Michael M. Bronstein.
Computer Graphics Forum (CGF) - Proc. EUROGRAPHICS 2017. May 2017, Vol. 36 Number 2. Published by Wiley & Sons.
C33. Geometric Deep Learning
Jonathan Masci, Emanuele Rodolà, Davide Boscaini, Michael M. Bronstein, and Hao Li.
Proc. SIGGRAPH Asia Courses (SIGA 2016).
C32. Computing and Processing Correspondences with Functional Maps
Maks Ovsjanikov, Etienne Corman, Michael M. Bronstein, Emanuele Rodolà, Mirela Ben-Chen, Leonidas Guibas, Frederic Chazal, and Alex M. Bronstein.
Proc. SIGGRAPH Asia Courses (SIGA 2016).
C31. Learning Shape Correspondence with Anisotropic Convolutional Neural Networks
Davide Boscaini, Jonathan Masci, Emanuele Rodolà, and Michael M. Bronstein.
Neural Information Processing Systems (NIPS 2016).
C30. Matching Deformable Objects in Clutter
Luca Cosmo, Emanuele Rodolà, Jonathan Masci, Andrea Torsello, and Michael M. Bronstein.
Intl. Conference on 3D Vision (3DV 2016).
C29. Shape Analysis with Anisotropic Windowed Fourier Transform
Simone Melzi, Emanuele Rodolà, Umberto Castellani, and Michael M. Bronstein.
Intl. Conference on 3D Vision (3DV 2016).
C28. Coupled Functional Maps
Davide Eynard, Emanuele Rodolà, Klaus Glashoff, and Michael M. Bronstein.
Intl. Conference on 3D Vision (3DV 2016).
J12. Non-Rigid Puzzles
Or Litany, Emanuele Rodolà, Alex M. Bronstein, Michael M. Bronstein, and Daniel Cremers.
Computer Graphics Forum (CGF) - Proc. SGP 2016. 2016, Vol. 35 Number 5. Published by Wiley & Sons - Best Paper Award.
J11. An Accurate and Robust Artificial Marker Based on Cyclic Codes
Filippo Bergamasco, Andrea Albarelli, Luca Cosmo, Emanuele Rodolà, and Andrea Torsello.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). 2016
C27. A Game-theoretical Approach for Joint Matching of Multiple Feature throughout Unordered Images
Luca Cosmo, Andrea Albarelli, Filippo Bergamasco, Andrea Torsello, Emanuele Rodolà, and Daniel Cremers.
Intl. Conference on Pattern Recognition (ICPR 2016).
C26. SHREC’16: Partial Matching of Deformable Shapes
Luca Cosmo, Emanuele Rodolà, Michael M. Bronstein, Andrea Torsello, Daniel Cremers, Yusuf Sahillioglu.
EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR 2016).
C25. SHREC’16: Matching of Deformable Shapes with Topological Noise
Zorah Lähner, Emanuele Rodolà, Michael M. Bronstein, Daniel Cremers, Oliver Burghard, Luca Cosmo, Alexander Dieckmann, Reinhard Klein, Yusuf Sahillioglu.
EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR 2016).
C24. Efficient Globally Optimal 2D-to-3D Deformable Shape Matching
Zorah Lähner, Emanuele Rodolà, Frank R. Schmidt, Michael Bronstein, and Daniel Cremers.
The XXIX IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016).
paper | code/data | poster | slides
J10. Anisotropic Diffusion Descriptors
Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael Bronstein, and Daniel Cremers.
Computer Graphics Forum (CGF) - Proc. EUROGRAPHICS 2016. May 2016, Vol. 35 Number 2, pp 431-441. Published by Wiley & Sons.
paper | code (steerable filters)
J09. Partial Functional Correspondence
Emanuele Rodolà, Luca Cosmo, Michael Bronstein, Andrea Torsello, and Daniel Cremers.
Computer Graphics Forum (CGF) - Presented at SGP 2016. Winner of the SHREC'16 Partial Correspondence contest (link)
paper | code | dataset | slides
J08. Consistent Partial Matching of Shape Collections via Sparse Modeling
Luca Cosmo, Emanuele Rodolà, Andrea Albarelli, Facundo Mémoli, and Daniel Cremers.
Computer Graphics Forum (CGF) - Presented at EUROGRAPHICS 2016.
C23. Point-wise Map Recovery and Refinement from Functional Correspondence
Emanuele Rodolà, Michael Moeller, and Daniel Cremers.
The 20th Intl. Symposium on Vision, Modeling and Visualization (VMV 2015) - Best Paper Award.
J07. Realistic Photometric Stereo Using Partial Differential Irradiance Equation Ratios
Roberto Mecca, Emanuele Rodolà, and Daniel Cremers.
Computers & Graphics (CAG) - Proc. SMI 2015. October 2015, Vol. 51, pp 8-16. Published by Elsevier.
J06. A Simple and Effective Relevance-based Point Sampling for 3D Shapes
Emanuele Rodolà, Andrea Albarelli, Daniel Cremers, and Andrea Torsello.
Pattern Recognition Letters (PRL). July 2015, Vol. 59, pp 41-47. Published by Elsevier.
C22. Adopting an Unconstrained Ray Model in Light-Field Cameras for 3D Shape Reconstruction
Filippo Bergamasco, Andrea Albarelli, Luca Cosmo, Andrea Torsello, Emanuele Rodolà, and Daniel Cremers.
The XXVIII IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015)
C21. Analysis of Surface Parametrizations for Modern Photometric Stereo Modeling
Roberto Mecca, Emanuele Rodolà, and Daniel Cremers.
The 12th International Conference on Quality Control by Artificial Vision – Photometric Stereo: from Theory to Industrial Applications (QCAV 2015 Workshops)
J05. Fast and Accurate Surface Alignment Through an Isometry-Enforcing Game
Andrea Albarelli, Emanuele Rodolà, and Andrea Torsello.
Pattern Recognition (PR). July 2015, Vol. 48 Issue 7, pp 2209-2226. Published by Elsevier.
J04. Robust Region Detection via Consensus Segmentation of Deformable Shapes
Emanuele Rodolà, Samuel Rota Bulò, and Daniel Cremers.
Computer Graphics Forum (CGF) - Proc. SGP 2014. August 2014, Vol. 33 Number 5, pp 97-106. Published by Wiley & Sons.
C20. Anisotropic Laplace-Beltrami Operators for Shape Analysis
Mathieu Andreux, Emanuele Rodolà, Mathieu Aubry, and Daniel Cremers.
Sixth Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment (NORDIA 2014) – oral.
C19. Optimal Intrinsic Descriptors for Non-Rigid Shape Analysis
Thomas Windheuser, Matthias Vestner, Emanuele Rodolà, Rudolph Triebel, and Daniel Cremers.
The 25th British Machine Vision Conference (BMVC 2014).
C18. Learning Similarities for Rigid and Non-Rigid Object Detection
Asako Kanezaki, Emanuele Rodolà, Daniel Cremers, and Tatsuya Harada.
The 2014 International Conference on 3D Vision (3DV 2014).
C17. Dense Non-Rigid Shape Correspondence Using Random Forests
Emanuele Rodolà, Samuel Rota Bulò, Thomas Windheuser, Matthias Vestner, and Daniel Cremers.
The XXVII IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014).
paper | code | dataset (KIDS) | video
C16. Elastic Net Constraints for Shape Matching
Emanuele Rodolà, Andrea Torsello, Tatsuya Harada, Yasuo Kuniyoshi, and Daniel Cremers.
The 2013 International Conference on Computer Vision (ICCV 2013).
Also presented at Discrete Curvature – Theory and Applications. 18-22 November 2013, Marseille (France)
C15. Efficient Shape Matching Using Vector Extrapolation
Emanuele Rodolà, Tatsuya Harada, Yasuo Kuniyoshi, and Daniel Cremers.
The 24th British Machine Vision Conference (BMVC 2013) – oral (acceptance rate 7%).
C14. Can a Fully Unconstrained Imaging Model be Applied Effectively to Central Cameras?
Filippo Bergamasco, Andrea Albarelli, Emanuele Rodolà, and Andrea Torsello.
The XXVI IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013).
J03. Stable and Fast Techniques for Unambiguous Compound Phase Coding
Andrea Torsello, Andrea Albarelli, and Emanuele Rodolà.
Image and Vision Computing (IVC). April 2013, Vol.41 Issue 4, pp 341-356. Published by Elsevier.
J02. A Scale Independent Selection Process for 3D Object Recognition in Cluttered Scenes
Emanuele Rodolà, Andrea Albarelli, Filippo Bergamasco, and Andrea Torsello.
International Journal of Computer Vision (IJCV) – Special Issue on 3D Imaging, Processing and Modeling Techniques. March 2013, Vol.102 Issue 1, pp 129-145. Published by Springer US.
C13. A Game-Theoretic Approach to Deformable Shape Matching
Emanuele Rodolà, Alex M. Bronstein, Andrea Albarelli, Filippo Bergamasco, and Andrea Torsello.
The XXV IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012).
J01. Imposing Semi-local Geometric Constraints for Accurate Correspondences Selection in Structure from Motion: a Game-Theoretic Perspective
Andrea Albarelli, Emanuele Rodolà, and Andrea Torsello.
International Journal of Computer Vision (IJCV) – Special Issue on 3D Data Processing, Visualization and Transmission. March 2012, Vol.97 Issue 1, pp 36-53. Published by Springer US - Invited Paper.
C12. Multiview Registration via Graph Diffusion of Dual Quaternions
Andrea Torsello, Emanuele Rodolà, and Andrea Albarelli.
The XXIV IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011).
C11. RUNE-Tag: a High Accuracy Fiducial Marker with Strong Occlusion Resilience
Filippo Bergamasco, Andrea Albarelli, Emanuele Rodolà, Andrea Torsello.
The XXIV IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011).
C10. A Non-Cooperative Game for 3D Object Recognition in Cluttered Scenes
Andrea Albarelli, Emanuele Rodolà, Filippo Bergamasco, and Andrea Torsello.
The First Joint 3DIM/3DPVT Conference 3D Imaging, Modeling, Processing, Visualization, Transmission (3DIMPVT 2011).
C09. Sampling Relevant Points for Surface Registration
Andrea Torsello, Emanuele Rodolà, and Andrea Albarelli.
The First Joint 3DIM/3DPVT Conference 3D Imaging, Modeling, Processing, Visualization, Transmission (3DIMPVT 2011).
C08. Loosely Distinctive Features for Robust Surface Alignment
Andrea Albarelli, Emanuele Rodolà, and Andrea Torsello.
11th European Conference on Computer Vision (ECCV 2010).
C07. Robust Camera Calibration Using Inaccurate Targets
Andrea Albarelli, Emanuele Rodolà, and Andrea Torsello.
21st British Machine Vision Conference 2010 (BMVC 2010).
C06. A Game-Theoretic Approach to the Enforcement of Global Consistency in Multi-View Feature Matching
Emanuele Rodolà, Andrea Torsello, and Andrea Albarelli.
13th International Workshop on Structural and Syntactic Pattern Recognition and 8th International Workshop on Statistical Pattern Recognition (S+SSPR 2010) - oral.
C05. A Game-Theoretic Approach to Robust Selection of Multi-View Point Correspondence
Emanuele Rodolà, Andrea Albarelli, and Andrea Torsello.
20th International Conference on Pattern Recognition (ICPR 2010) – oral (acceptance rate 18%).
C04. Robust Figure Extraction on Textured Background: a Game-Theoretic Approach
Andrea Albarelli, Emanuele Rodolà, Alberto Cavallarin, and Andrea Torsello.
20th International Conference on Pattern Recognition (ICPR 2010).
C03. A Game-Theoretic Approach to Fine Surface Registration without Initial Motion Estimation
Andrea Albarelli, Emanuele Rodolà, and Andrea Torsello.
The XXIII IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010).
C02. Robust Game-Theoretic Inlier Selection for Bundle Adjustment
Andrea Albarelli, Emanuele Rodolà, and Andrea Torsello.
Fifth International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT 2010) - Nvidia Best Student Paper Award.
C01. Fast 3D Surface Reconstruction by Unambiguous Compound Phase Coding
Andrea Albarelli, Emanuele Rodolà, Samuel Rota Bulò, and Andrea Torsello.
The 2009 IEEE International Workshop on 3-D Digital Imaging and Modeling (3DIM 2009).
Also presented at 7th Eurographics Italian Chapter Conference (EG_IT 2009).
Book chapters:
B02. "Applying random forests to the problem of dense non-rigid shape correspondence".
Matthias Vestner, Emanuele Rodolà, Samuel Rota Bulò, Thomas Windheuser, and Daniel Cremers.
Mathematics and Visualization - Perspectives in Shape Analysis.
Print ISBN 978-3-319-24724-3. Published by Springer, 2016.
B01. “A Game-Theoretic Approach to Pairwise Clustering and Matching”.
Marcello Pelillo, Samuel Rota Bulò, Andrea Torsello, Andrea Albarelli, and Emanuele Rodolà.
Similarity-Based Pattern Analysis and Recognition – Part III.
pp 179-216. Print ISBN 978-1-4471-5627-7. Online ISBN 978-1-4471-5628-4. DOI 10.1007/978-1-4471-5628-4_8. Published by Springer London, 2013.
Technical Reports, domestic conferences, preprints, etc.:
TR18. "Self-supervised learning of dense shape correspondence".
Oshri Halimi, Or Litany, Emanuele Rodolà, Alex Bronstein, and Ron Kimmel.
arXiv:1812.02415 – later accepted at CVPR 2019, see C42.
TR17. "Isospectralization, or how to hear shape, style, and correspondence".
Luca Cosmo, Mikhail Panine, Arianna Rampini, Maks Ovsjanikov, Michael Bronstein, and Emanuele Rodol à .
arXiv:1811.11465 – later accepted at CVPR 2019, see C43.
TR16. "Functional Maps Representation on Product Manifolds".
Emanuele Rodol à , Zorah L ähner, Alex Bronstein, Michael Bronstein, and Justin Solomon.
arXiv:1809.10940 – later accepted at CGF, see J17.
TR15. "FARM: Functional Automatic Registration Method for 3D Human Bodies".
Riccardo Marin, Simone Melzi, Emanuele Rodol à , and Umberto Castellani.
arXiv:1707.08991 – later published at 3DV 2017, see C39.
TR13. "Localized Manifold Harmonics for Spectral Shape Analysis".
Simone Melzi, Emanuele Rodolà, Umberto Castellani, and Michael Bronstein.
arXiv:1707.02596 – later accepted at CGF, see J15; also presented as SGP Posters (2017).
TR12. "Deep Functional Maps: Structured Prediction for Dense Shape Correspondence".
Or Litany, Tal Remez, Emanuele Rodolà, Alex Bronstein, and Michael Bronstein.
arXiv:1704.08686 (2017) – later published at ICCV 2017, see C37.
TR11. "Product Manifold Filter: Non-rigid shape correspondence via kernel density estimation in the product space".
Matthias Vestner, Roee Litman, Emanuele Rodolà, Alex Bronstein, and Daniel Cremers.
arXiv:1701.00669 (2017) – later published at CVPR 2017, see C36.
TR10. "Geometric deep learning on graphs and manifolds using mixture model CNNs".
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, and Michael Bronstein.
arXiv:1611.08402 (2016) – later published at CVPR 2017, see C35.
TR09. "Bayesian Inference of Bijective Non-Rigid Shape Correspondence".
Matthias Vestner, Roee Litman, Alex Bronstein, Emanuele Rodolà, and Daniel Cremers.
arXiv:1607.03425 (2016).
TR08. "Learning Shape Correspondence with Anisotropic Convolutional Neural Networks".
Davide Boscaini, Jonathan Masci, Emanuele Rodolà, and Michael Bronstein.
arXiv:1605.06437 (2016) – later published at NIPS 2016, see C28.
TR07. "Efficient Globally Optimal 2D-to-3D Deformable Shape Matching".
Zorah Lähner, Emanuele Rodolà, Frank R. Schmidt, Michael Bronstein, and Daniel Cremers.
arXiv:1601.06070 (2016) – later published at CVPR 2016, see C24.
TR06. "Point-wise Map Recovery and Refinement from Functional Correspondence".
Emanuele Rodolà, Michael Moeller, and Daniel Cremers.
arXiv:1506.05603 (2015) – later published at VMV 2015, see C23.
TR05. "Partial Functional Correspondence".
Emanuele Rodolà, Luca Cosmo, Michael Bronstein, Andrea Torsello, and Daniel Cremers.
arXiv:1506.05274 (2015) – later published in the Computer Graphics Forum, see J09.
TR04. "グラフマッチング学習を用いたRGB-D画像からの物体検出".
Asako Kanezaki, Emanuele Rodolà, and Tatsuya Harada.
第20回ロボティクスシンポジア – Robotics Symposia (RS 2015).
TR03. "対応点集合類似度学習を用いた剛体・非剛体物体検出".
Asako Kanezaki, Emanuele Rodolà, Daniel Cremers, and Tatsuya Harada.
信学技報, vol. 114, no. 230, pp. 13-18, October. (PRMU 2014).
TR02. "RGB-D画像からの物体検出における対応点集合類似度の学習".
Asako Kanezaki, Emanuele Rodolà, and Tatsuya Harada.
第32回日本ロボット学会学術講演会 - The Robotics Society of Japan (RSJ 2014) - 2015 研究奨励賞 Encouragement Award.
TR01. "Relaxations for Minimizing Metric Distortion and Elastic Energies for 3D Shape Matching".
Daniel Cremers, Emanuele Rodolà, and Thomas Windheuser.
Actes des recontres du CIRM: Courbure discrete: théorie et applications.
2013, Vol. 3 Number 1, pp 107-117: (2013), pp. 107-117.
TR14. "Efficient Deformable Shape Correspondence via Kernel Matching".
Matthias Vestner, Zorah L ähner, Amit Boyarski, Or Litany, Ron Slossberg, Tal Remez, Emanuele Rodolà, Alex Bronstein, Michael Bronstein, Ron Kimmel, and Daniel Cremers.