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ORCIDMaks Ovsjanikov
Maksim Ovsjanikov
Person information
- affiliation: École polytechnique, Paris, France
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2020 – today
- 2024
[j58]Jarne Van den Herrewegen
, Tom Tourwé, Maks Ovsjanikov, Francis Wyffels:
Fine-tuning 3D foundation models for geometric object retrieval. Comput. Graph. 122: 103993 (2024)- [j57]Ramana Sundararaman, Nicolas Donati, Simone Melzi, Etienne Corman, Maks Ovsjanikov:
Deformation Recovery: Localized Learning for Detail-Preserving Deformations. ACM Trans. Graph. 43(6): 219:1-219:16 (2024) - [c70]Mariem Mezghanni, Malika Boulkenafed, Maks Ovsjanikov:
RIVQ-VAE: Discrete Rotation-Invariant 3D Representation Learning. 3DV 2024: 1382-1391 - [c69]Sara Hahner, Souhaib Attaiki, Jochen Garcke, Maks Ovsjanikov:
Unsupervised Representation Learning for Diverse Deformable Shape Collections. 3DV 2024: 1594-1604 - [c68]Nissim Maruani, Maks Ovsjanikov, Pierre Alliez, Mathieu Desbrun:
PoNQ: A Neural QEM-Based Mesh Representation. CVPR 2024: 3647-3657 - [c67]Robin Magnet, Maks Ovsjanikov:
Memory-Scalable and Simplified Functional Map Learning. CVPR 2024: 4041-4050 - [c66]Thomas Wimmer, Peter Wonka, Maks Ovsjanikov:
Back to 3D: Few-Shot 3D Keypoint Detection with Back-Projected 2D Features. CVPR 2024: 4154-4164 - [c65]Ramana Sundararaman, Roman Klokov, Maks Ovsjanikov:
Self-Supervised Dual Contouring. CVPR 2024: 4681-4691 - [c64]Souhail Hadgi, Lei Li, Maks Ovsjanikov:
To Supervise or Not to Supervise: Understanding and Addressing the Key Challenges of Point Cloud Transfer Learning. ECCV (78) 2024: 146-163 - [i67]Maysam Behmanesh, Maks Ovsjanikov:
Smoothed Graph Contrastive Learning via Seamless Proximity Integration. CoRR abs/2402.15270 (2024) - [i66]Souhaib Attaiki, Maks Ovsjanikov:
Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction. CoRR abs/2403.06804 (2024) - [i65]Nissim Maruani, Maks Ovsjanikov, Pierre Alliez, Mathieu Desbrun:
PoNQ: a Neural QEM-based Mesh Representation. CoRR abs/2403.12870 (2024) - [i64]Souhail Hadgi, Lei Li, Maks Ovsjanikov:
To Supervise or Not to Supervise: Understanding and Addressing the Key Challenges of 3D Transfer Learning. CoRR abs/2403.17869 (2024) - [i63]Robin Magnet, Maks Ovsjanikov:
Memory-Scalable and Simplified Functional Map Learning. CoRR abs/2404.00330 (2024) - [i62]Ramana Sundararaman, Roman Klokov, Maks Ovsjanikov:
Self-Supervised Dual Contouring. CoRR abs/2405.18131 (2024) - [i61]Léopold Maillard, Nicolas Sereyjol-Garros, Tom Durand, Maks Ovsjanikov:
DeBaRA: Denoising-Based 3D Room Arrangement Generation. CoRR abs/2409.18336 (2024) - [i60]Ramana Sundararaman, Nicolas Donati, Simone Melzi, Etienne Corman, Maks Ovsjanikov:
Deformation Recovery: Localized Learning for Detail-Preserving Deformations. CoRR abs/2410.08225 (2024) - 2023
- [j56]Robin Magnet, Maks Ovsjanikov:
Scalable and Efficient Functional Map Computations on Dense Meshes. Comput. Graph. Forum 42(2): 89-101 (2023) - [j55]Lei Li, Hongbo Fu, Maks Ovsjanikov:
WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration. IEEE Trans. Vis. Comput. Graph. 29(7): 3368-3379 (2023) - [c63]Souhaib Attaiki, Maks Ovsjanikov:
Understanding and Improving Features Learned in Deep Functional Maps. CVPR 2023: 1316-1326 - [c62]Panos Achlioptas, Maks Ovsjanikov, Leonidas J. Guibas, Sergey Tulyakov:
Affection: Learning Affective Explanations for Real-World Visual Data. CVPR 2023: 6641-6651 - [c61]Souhaib Attaiki, Lei Li, Maks Ovsjanikov:
Generalizable Local Feature Pre-training for Deformable Shape Analysis. CVPR 2023: 13650-13661 - [c60]Mingze Sun, Shiwei Mao, Puhua Jiang, Maks Ovsjanikov, Ruqi Huang:
Spatially and Spectrally Consistent Deep Functional Maps. ICCV 2023: 14451-14461 - [c59]Nissim Maruani, Roman Klokov, Maks Ovsjanikov, Pierre Alliez, Mathieu Desbrun:
VoroMesh: Learning Watertight Surface Meshes with Voronoi Diagrams. ICCV 2023: 14519-14528 - [c58]Ahmed Abdelreheem, Ivan Skorokhodov, Maks Ovsjanikov, Peter Wonka:
SATR: Zero-Shot Semantic Segmentation of 3D Shapes. ICCV 2023: 15120-15133 - [c57]Maysam Behmanesh, Maximilian Krahn, Maks Ovsjanikov:
TIDE: Time Derivative Diffusion for Deep Learning on Graphs. ICML 2023: 2015-2030 - [c56]Souhaib Attaiki, Maks Ovsjanikov:
Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction. NeurIPS 2023 - [c55]Ahmed Abdelreheem, Abdelrahman Eldesokey, Maks Ovsjanikov, Peter Wonka:
Zero-Shot 3D Shape Correspondence. SIGGRAPH Asia 2023: 59:1-59:11 - [i59]Souhaib Attaiki, Maks Ovsjanikov:
NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching. CoRR abs/2301.05839 (2023) - [i58]Robin Magnet, Maks Ovsjanikov:
Scalable and Efficient Functional Map Computations on Dense Meshes. CoRR abs/2303.05965 (2023) - [i57]Souhaib Attaiki, Lei Li, Maks Ovsjanikov:
Generalizable Local Feature Pre-training for Deformable Shape Analysis. CoRR abs/2303.15104 (2023) - [i56]Souhaib Attaiki, Maks Ovsjanikov:
Understanding and Improving Features Learned in Deep Functional Maps. CoRR abs/2303.16527 (2023) - [i55]Ahmed Abdelreheem, Ivan Skorokhodov, Maks Ovsjanikov, Peter Wonka:
SATR: Zero-Shot Semantic Segmentation of 3D Shapes. CoRR abs/2304.04909 (2023) - [i54]Ahmed Abdelreheem, Abdelrahman Eldesokey, Maks Ovsjanikov, Peter Wonka:
Zero-Shot 3D Shape Correspondence. CoRR abs/2306.03253 (2023) - [i53]Mingze Sun, Shiwei Mao, Puhua Jiang, Maks Ovsjanikov, Ruqi Huang:
Spatially and Spectrally Consistent Deep Functional Maps. CoRR abs/2308.08871 (2023) - [i52]Nissim Maruani, Roman Klokov, Maks Ovsjanikov, Pierre Alliez, Mathieu Desbrun:
VoroMesh: Learning Watertight Surface Meshes with Voronoi Diagrams. CoRR abs/2308.14616 (2023) - [i51]Vincent Mallet, Souhaib Attaiki, Maks Ovsjanikov:
AtomSurf : Surface Representation for Learning on Protein Structures. CoRR abs/2309.16519 (2023) - [i50]Sara Hahner, Souhaib Attaiki, Jochen Garcke, Maks Ovsjanikov:
Unsupervised Representation Learning for Diverse Deformable Shape Collections. CoRR abs/2310.18141 (2023) - [i49]Thomas Wimmer, Peter Wonka, Maks Ovsjanikov:
Back to 3D: Few-Shot 3D Keypoint Detection with Back-Projected 2D Features. CoRR abs/2311.18113 (2023) - 2022
- [j54]Nicolas Donati, Etienne Corman, Simone Melzi, Maks Ovsjanikov:
Complex Functional Maps: A Conformal Link Between Tangent Bundles. Comput. Graph. Forum 41(1): 317-334 (2022) - [j53]Luca Moschella, Simone Melzi, Luca Cosmo, Filippo Maggioli, Or Litany, Maks Ovsjanikov, Leonidas J. Guibas, Emanuele Rodolà:
Learning Spectral Unions of Partial Deformable 3D Shapes. Comput. Graph. Forum 41(2): 407-417 (2022) - [j52]Mikhail Panine, Maxime Kirgo, Maks Ovsjanikov:
Non-Isometric Shape Matching via Functional Maps on Landmark-Adapted Bases. Comput. Graph. Forum 41(6): 394-417 (2022) - [j51]Nicholas Sharp, Souhaib Attaiki, Keenan Crane, Maks Ovsjanikov:
DiffusionNet: Discretization Agnostic Learning on Surfaces. ACM Trans. Graph. 41(3): 27:1-27:16 (2022) - [c54]Lei Li, Souhaib Attaiki, Maks Ovsjanikov:
SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence. 3DV 2022: 144-154 - [c53]Robin Magnet, Jing Ren, Olga Sorkine-Hornung, Maks Ovsjanikov:
Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy Optimization. 3DV 2022: 495-504 - [c52]Nicolas Donati, Etienne Corman, Maks Ovsjanikov:
Deep orientation-aware functional maps: Tackling symmetry issues in Shape Matching. CVPR 2022: 732-741 - [c51]Mariem Mezghanni, Théo Bodrito, Malika Boulkenafed, Maks Ovsjanikov:
Physical Simulation Layer for Accurate 3D Modeling. CVPR 2022: 13504-13513 - [c50]Ramana Sundararaman, Gautam Pai, Maks Ovsjanikov:
Implicit Field Supervision for Robust Non-rigid Shape Matching. ECCV (3) 2022: 344-362 - [c49]Emanuele Rodolà, Luca Cosmo, Maks Ovsjanikov, Arianna Rampini, Simone Melzi, Michael M. Bronstein, Riccardo Marin:
Inverse Computational Spectral Geometry. Eurographics (Tutorials) 2022 - [c48]Abhishek Sharma, Maks Ovsjanikov:
Matrix Decomposition on Graphs: A Simplified Functional View. ICASSP 2022: 3358-3362 - [c47]Lei Li, Nicolas Donati, Maks Ovsjanikov:
Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching. NeurIPS 2022 - [c46]Souhaib Attaiki, Maks Ovsjanikov:
NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching. NeurIPS 2022 - [c45]Ramana Sundararaman, Riccardo Marin, Emanuele Rodolà, Maks Ovsjanikov:
Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching. NeurIPS 2022 - [i48]Ramana Sundararaman, Gautam Pai, Maks Ovsjanikov:
Implicit field supervision for robust non-rigid shape matching. CoRR abs/2203.07694 (2022) - [i47]Nicolas Donati, Etienne Corman, Maks Ovsjanikov:
Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matching. CoRR abs/2204.13453 (2022) - [i46]Mikhail Panine, Maxime Kirgo, Maks Ovsjanikov:
Non-Isometric Shape Matching via Functional Maps on Landmark-Adapted Bases. CoRR abs/2205.04800 (2022) - [i45]Lei Li, Souhaib Attaiki, Maks Ovsjanikov:
SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence. CoRR abs/2209.07806 (2022) - [i44]Panos Achlioptas, Maks Ovsjanikov, Leonidas J. Guibas, Sergey Tulyakov:
Affection: Learning Affective Explanations for Real-World Visual Data. CoRR abs/2210.01946 (2022) - [i43]Robin Magnet, Jing Ren, Olga Sorkine-Hornung, Maks Ovsjanikov:
Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy Optimization. CoRR abs/2210.02870 (2022) - [i42]Lei Li, Nicolas Donati, Maks Ovsjanikov:
Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching. CoRR abs/2210.06373 (2022) - [i41]Ramana Sundararaman, Riccardo Marin, Emanuele Rodolà, Maks Ovsjanikov:
Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching. CoRR abs/2211.14604 (2022) - [i40]Adrien Poulenard, Maks Ovsjanikov, Leonidas J. Guibas:
Equivalence Between SE(3) Equivariant Networks via Steerable Kernels and Group Convolution. CoRR abs/2211.15903 (2022) - [i39]Maximilian Krahn, Maysam Behmanesh, Maks Ovsjanikov:
TIDE: Time Derivative Diffusion for Deep Learning on Graphs. CoRR abs/2212.02483 (2022) - 2021
- [j50]Maxime Kirgo, Simone Melzi, Giuseppe Patanè, Emanuele Rodolà, Maks Ovsjanikov:
Wavelet-based Heat Kernel Derivatives: Towards Informative Localized Shape Analysis. Comput. Graph. Forum 40(1): 165-179 (2021) - [j49]Filippo Maggioli, Simone Melzi, Maksim Ovsjanikov, Michael M. Bronstein, Emanuele Rodolà:
Orthogonalized Fourier Polynomials for Signal Approximation and Transfer. Comput. Graph. Forum 40(2): 435-447 (2021) - [j48]Jing Ren, Simone Melzi, Peter Wonka, Maks Ovsjanikov:
Discrete Optimization for Shape Matching. Comput. Graph. Forum 40(5): 81-96 (2021) - [j47]Riccardo Marin, Arianna Rampini, Umberto Castellani, Emanuele Rodolà, Maks Ovsjanikov, Simone Melzi:
Spectral Shape Recovery and Analysis Via Data-driven Connections. Int. J. Comput. Vis. 129(10): 2745-2760 (2021) - [j46]Jing Ren, Biao Zhang, Bojian Wu, Jianqiang Huang, Lubin Fan, Maks Ovsjanikov, Peter Wonka:
Intuitive and efficient roof modeling for reconstruction and synthesis. ACM Trans. Graph. 40(6): 249:1-249:17 (2021) - [j45]Marie-Julie Rakotosaona, Noam Aigerman, Niloy J. Mitra, Maks Ovsjanikov, Paul Guerrero:
Differentiable surface triangulation. ACM Trans. Graph. 40(6): 267:1-267:13 (2021) - [c44]Souhaib Attaiki, Gautam Pai, Maks Ovsjanikov:
DPFM: Deep Partial Functional Maps. 3DV 2021: 175-185 - [c43]Marie-Julie Rakotosaona, Paul Guerrero, Noam Aigerman, Niloy J. Mitra, Maks Ovsjanikov:
Learning Delaunay Surface Elements for Mesh Reconstruction. CVPR 2021: 22-31 - [c42]Gautam Pai, Jing Ren, Simone Melzi, Peter Wonka, Maks Ovsjanikov:
Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence With Functional Maps. CVPR 2021: 384-393 - [c41]Mariem Mezghanni, Malika Boulkenafed, André Lieutier, Maks Ovsjanikov:
Physically-Aware Generative Network for 3D Shape Modeling. CVPR 2021: 9330-9341 - [c40]Panos Achlioptas, Maks Ovsjanikov, Kilichbek Haydarov, Mohamed Elhoseiny, Leonidas J. Guibas:
ArtEmis: Affective Language for Visual Art. CVPR 2021: 11569-11579 - [c39]Robin Magnet, Maks Ovsjanikov:
DWKS : A Local Descriptor of Deformations Between Meshes and Point Clouds. ICCV 2021: 3773-3782 - [i38]Panos Achlioptas, Maks Ovsjanikov, Kilichbek Haydarov, Mohamed Elhoseiny, Leonidas J. Guibas:
ArtEmis: Affective Language for Visual Art. CoRR abs/2101.07396 (2021) - [i37]Abhishek Sharma, Maks Ovsjanikov:
Matrix Decomposition on Graphs: A Functional View. CoRR abs/2102.03233 (2021) - [i36]Luca Moschella, Simone Melzi, Luca Cosmo, Filippo Maggioli, Or Litany, Maks Ovsjanikov, Leonidas J. Guibas, Emanuele Rodolà:
Spectral Unions of Partial Deformable 3D Shapes. CoRR abs/2104.00514 (2021) - [i35]Lei Li, Hongbo Fu, Maks Ovsjanikov:
UPDesc: Unsupervised Point Descriptor Learning for Robust Registration. CoRR abs/2108.02740 (2021) - [i34]Jing Ren, Biao Zhang, Bojian Wu, Jianqiang Huang, Lubin Fan, Maks Ovsjanikov, Peter Wonka:
Intuitive and Efficient Roof Modeling for Reconstruction and Synthesis. CoRR abs/2109.07683 (2021) - [i33]Marie-Julie Rakotosaona, Noam Aigerman, Niloy J. Mitra, Maks Ovsjanikov, Paul Guerrero:
Differentiable Surface Triangulation. CoRR abs/2109.10695 (2021) - [i32]Abhishek Sharma, Maks Ovsjanikov:
Learning Canonical Embedding for Non-rigid Shape Matching. CoRR abs/2110.02994 (2021) - [i31]Souhaib Attaiki, Gautam Pai, Maks Ovsjanikov:
DPFM: Deep Partial Functional Maps. CoRR abs/2110.09994 (2021) - [i30]Abhishek Sharma, Maks Ovsjanikov:
Joint Symmetry Detection and Shape Matching for Non-Rigid Point Cloud. CoRR abs/2112.02713 (2021) - [i29]Riccardo Marin, Souhaib Attaiki, Simone Melzi, Emanuele Rodolà, Maks Ovsjanikov:
Why you should learn functional basis. CoRR abs/2112.07289 (2021) - [i28]Nicolas Donati, Etienne Corman, Simone Melzi, Maks Ovsjanikov:
Complex Functional Maps : a Conformal Link Between Tangent Bundles. CoRR abs/2112.09546 (2021) - 2020
- [j44]Marie-Julie Rakotosaona, Vittorio La Barbera, Paul Guerrero, Niloy J. Mitra, Maks Ovsjanikov:
PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds. Comput. Graph. Forum 39(1): 185-203 (2020) - [j43]Thibault Lescoat, Hsueh-Ti Derek Liu, Jean-Marc Thiery, Alec Jacobson, Tamy Boubekeur, Maks Ovsjanikov:
Spectral Mesh Simplification. Comput. Graph. Forum 39(2): 315-324 (2020) - [j42]Ruqi Huang, Jing Ren, Peter Wonka, Maks Ovsjanikov:
Consistent ZoomOut: Efficient Spectral Map Synchronization. Comput. Graph. Forum 39(5): 265-278 (2020) - [j41]Jing Ren, Simone Melzi, Maks Ovsjanikov, Peter Wonka:
MapTree: recovering multiple solutions in the space of maps. ACM Trans. Graph. 39(6): 264:1-264:17 (2020) - [c38]Riccardo Marin, Arianna Rampini, Umberto Castellani, Emanuele Rodolà, Maks Ovsjanikov, Simone Melzi:
Instant recovery of shape from spectrum via latent space connections. 3DV 2020: 120-129 - [c37]Nicolas Donati, Abhishek Sharma, Maks Ovsjanikov:
Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence. CVPR 2020: 8589-8598 - [c36]Marie-Julie Rakotosaona, Maks Ovsjanikov:
Intrinsic Point Cloud Interpolation via Dual Latent Space Navigation. ECCV (2) 2020: 655-672 - [c35]Nicholas Sharp, Maks Ovsjanikov:
PointTriNet: Learned Triangulation of 3D Point Sets. ECCV (23) 2020: 762-778 - [c34]Riccardo Marin, Marie-Julie Rakotosaona, Simone Melzi, Maks Ovsjanikov:
Correspondence learning via linearly-invariant embedding. NeurIPS 2020 - [c33]Abhishek Sharma, Maks Ovsjanikov:
Weakly Supervised Deep Functional Maps for Shape Matching. NeurIPS 2020 - [i27]Riccardo Marin, Arianna Rampini, Umberto Castellani, Emanuele Rodolà, Maks Ovsjanikov, Simone Melzi:
Instant recovery of shape from spectrum via latent space connections. CoRR abs/2003.06523 (2020) - [i26]Nicolas Donati, Abhishek Sharma, Maks Ovsjanikov:
Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence. CoRR abs/2003.14286 (2020) - [i25]Marie-Julie Rakotosaona, Maks Ovsjanikov:
Intrinsic Point Cloud Interpolation via Dual Latent Space Navigation. CoRR abs/2004.01661 (2020) - [i24]Nicholas Sharp, Maks Ovsjanikov:
PointTriNet: Learned Triangulation of 3D Point Sets. CoRR abs/2005.02138 (2020) - [i23]Jing Ren, Simone Melzi, Maks Ovsjanikov, Peter Wonka:
MapTree: Recovering Multiple Solutions in the Space of Maps. CoRR abs/2006.02532 (2020) - [i22]Maxime Kirgo, Simone Melzi, Giuseppe Patanè, Emanuele Rodolà, Maks Ovsjanikov:
Wavelet-based Heat Kernel Derivatives: Towards Informative Localized Shape Analysis. CoRR abs/2007.11632 (2020) - [i21]Abhishek Sharma, Maks Ovsjanikov:
Weakly Supervised Deep Functional Map for Shape Matching. CoRR abs/2009.13339 (2020) - [i20]Abhishek Sharma, Maks Ovsjanikov:
Geometric Matrix Completion: A Functional View. CoRR abs/2009.14343 (2020) - [i19]Jing Ren, Mikhail Panine, Peter Wonka, Maks Ovsjanikov:
Structured Regularization of Functional Map Computations. CoRR abs/2009.14624 (2020) - [i18]Riccardo Marin, Marie-Julie Rakotosaona, Simone Melzi, Maks Ovsjanikov:
Correspondence Learning via Linearly-invariant Embedding. CoRR abs/2010.13136 (2020) - [i17]Nicholas Sharp, Souhaib Attaiki, Keenan Crane, Maks Ovsjanikov:
Diffusion is All You Need for Learning on Surfaces. CoRR abs/2012.00888 (2020) - [i16]Marie-Julie Rakotosaona, Paul Guerrero, Noam Aigerman, Niloy J. Mitra, Maks Ovsjanikov:
Learning Delaunay Surface Elements for Mesh Reconstruction. CoRR abs/2012.01203 (2020)
2010 – 2019
- 2019
- [j40]Yanir Kleiman, Maks Ovsjanikov:
Robust Structure-Based Shape Correspondence. Comput. Graph. Forum 38(1): 7-20 (2019) - [j39]Jing Ren, Mikhail Panine, Peter Wonka, Maks Ovsjanikov:
Structured Regularization of Functional Map Computations. Comput. Graph. Forum 38(5): 39-53 (2019) - [j38]Ruqi Huang, Panos Achlioptas, Leonidas J. Guibas, Maks Ovsjanikov:
Limit Shapes - A Tool for Understanding Shape Differences and Variability in 3D Model Collections. Comput. Graph. Forum 38(5): 187-202 (2019) - [j37]Etienne Corman, Maks Ovsjanikov:
Functional Characterization of Deformation Fields. ACM Trans. Graph. 38(1): 8:1-8:19 (2019) - [j36]Hsueh-Ti Derek Liu, Alec Jacobson, Maks Ovsjanikov:
Spectral coarsening of geometric operators. ACM Trans. Graph. 38(4): 105:1-105:13 (2019) - [j35]Simone Melzi, Jing Ren, Emanuele Rodolà, Abhishek Sharma, Peter Wonka, Maks Ovsjanikov:
ZoomOut: spectral upsampling for efficient shape correspondence. ACM Trans. Graph. 38(6): 155:1-155:14 (2019) - [c32]Arianna Rampini, Irene Tallini, Maks Ovsjanikov, Alexander M. Bronstein, Emanuele Rodolà:
Correspondence-Free Region Localization for Partial Shape Similarity via Hamiltonian Spectrum Alignment. 3DV 2019: 37-46 - [c31]Adrien Poulenard, Marie-Julie Rakotosaona, Yann Ponty, Maks Ovsjanikov:
Effective Rotation-Invariant Point CNN with Spherical Harmonics Kernels. 3DV 2019: 47-56 - [c30]Simone Melzi, Riccardo Marin, Emanuele Rodolà, Umberto Castellani, Jing Ren, Adrien Poulenard, Peter Wonka, Maks Ovsjanikov:
Matching Humans with Different Connectivity. 3DOR@Eurographics 2019: 121-128 - [c29]Luca Cosmo, Mikhail Panine, Arianna Rampini, Maks Ovsjanikov, Michael M. Bronstein, Emanuele Rodolà:
Isospectralization, or How to Hear Shape, Style, and Correspondence. CVPR 2019: 7529-7538 - [c28]Jean-Michel Roufosse, Abhishek Sharma, Maks Ovsjanikov:
Unsupervised Deep Learning for Structured Shape Matching. ICCV 2019: 1617-1627 - [c27]Ruqi Huang, Marie-Julie Rakotosaona, Panos Achlioptas, Leonidas J. Guibas, Maks Ovsjanikov:
OperatorNet: Recovering 3D Shapes From Difference Operators. ICCV 2019: 8587-8596 - [c26]Thibault Lescoat, Pooran Memari, Jean-Marc Thiery, Maks Ovsjanikov, Tamy Boubekeur:
Connectivity-preserving Smooth Surface Filling with Sharp Features. PG (Short Papers) 2019: 7-13 - [i15]Marie-Julie Rakotosaona, Vittorio La Barbera, Paul Guerrero, Niloy J. Mitra, Maks Ovsjanikov:
POINTCLEANNET: Learning to Denoise and Remove Outliers from Dense Point Clouds. CoRR abs/1901.01060 (2019) - [i14]