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Maks Ovsjanikov

Maks Ovsjanikov

D-Index & Metrics

Computer Science

D-Index
44
Citations
10324
World Ranking
7463
National Ranking
168

Overview

Maks Ovsjanikov is affiliated with École Polytechnique in France and has a significant record of publications primarily focused on computer science and engineering disciplines. Their research intersects various subfields including computer vision and pattern recognition, computational mechanics, and computer graphics and computer-aided design.

Their recent publications cover topics related to 3D shape modeling, analysis, and visualization. Some notable papers include:

  • DiffusionNet: Discretization Agnostic Learning on Surfaces, 2022, ACM Transactions on Graphics
  • Discrete Optimization for Shape Matching, 2021, Computer Graphics Forum
  • Consistent ZoomOut: Efficient Spectral Map Synchronization, 2020, Computer Graphics Forum
  • MapTree, 2020, ACM Transactions on Graphics
  • WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration, 2022, IEEE Transactions on Visualization and Computer Graphics

Their frequent coauthors reflect long-term collaborative efforts in the field, including Simone Melzi, Souhaib Attaiki, Jing Ren, Peter Wonka, and Emanuele Rodolà, with multiple joint publications recorded.

Ovsjanikov's contributions appear extensively in specific publication venues such as:

  • arXiv (Cornell University)
  • Computer Graphics Forum
  • ACM Transactions on Graphics
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • IEEE Transactions on Visualization and Computer Graphics

Their work predominantly addresses topics like 3D shape modeling and analysis, computer graphics and visualization techniques, image processing and 3D reconstruction, human pose and action recognition, and 3D surveying and cultural heritage. Other areas include advanced numerical analysis techniques and image retrieval and classification techniques.

The scope of Maks Ovsjanikov's research spans technical challenges at the intersection of geometric data analysis and computer vision, with a specialized focus on 3D representations and computational methods applied to shape understanding and matching.

Best Publications

  • A concise and provably informative multi-scale signature based on heat diffusion

    Jian Sun;Maks Ovsjanikov;Leonidas Guibas

  • Shape google: Geometric words and expressions for invariant shape retrieval

    Alexander M. Bronstein;Michael M. Bronstein;Leonidas J. Guibas;Maks Ovsjanikov

  • Functional maps: a flexible representation of maps between shapes

    Maks Ovsjanikov;Mirela Ben-Chen;Justin Solomon;Adrian Butscher

  • One Point Isometric Matching with the Heat Kernel

    Maks Ovsjanikov;Quentin Mérigot;Facundo Mémoli;Leonidas J. Guibas

  • PCPNET: Learning Local Shape Properties from Raw Point Clouds

    Paul Guerrero;Yanir Kleiman;Maks Ovsjanikov;Niloy J. Mitra

  • PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds

    Marie-Julie Rakotosaona;Vittorio La Barbera;Paul Guerrero;Niloy J. Mitra

  • Global intrinsic symmetries of shapes

    Maks Ovsjanikov;Jian Sun;Leonidas Guibas

  • Voronoi-Based Curvature and Feature Estimation from Point Clouds

    Quentin Mérigot;M Ovsjanikov;L J Guibas

  • DiffusionNet: Discretization Agnostic Learning on Surfaces

    Nicholas Sharp;Souhaib Attaiki;Keenan Crane;Maks Ovsjanikov

  • ZoomOut: spectral upsampling for efficient shape correspondence

    Simone Melzi;Jing Ren;Emanuele Rodolà;Abhishek Sharma

  • Shape Google: a computer vision approach to isometry invariant shape retrieval

    Maks Ovsjanikov;Alexander M. Bronstein;Michael M. Bronstein;Leonidas J. Guibas

  • Map-based exploration of intrinsic shape differences and variability

    Raif M. Rustamov;Maks Ovsjanikov;Omri Azencot;Mirela Ben-Chen

  • Efficient reconstruction of nonrigid shape and motion from real-time 3D scanner data

    Michael Wand;Bart Adams;Maksim Ovsjanikov;Alexander Berner

  • Continuous and orientation-preserving correspondences via functional maps

    Jing Ren;Adrien Poulenard;Peter Wonka;Maks Ovsjanikov

  • Persistence-based segmentation of deformable shapes

    Primoz Skraba;Maks Ovsjanikov;Frederic Chazal;Leonidas Guibas

  • Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence

    Nicolas Donati;Abhishek Sharma;Maks Ovsjanikov

  • Persistence-Based Structural Recognition

    Chunyuan Li;Maks Ovsjanikov;Frederic Chazal

  • Informative Descriptor Preservation via Commutativity for Shape Matching

    Dorian Nogneng;Maks Ovsjanikov

  • Dynamic geometry registration

    Niloy J. Mitra;Simon Flöry;Maks Ovsjanikov;Natasha Gelfand

  • Unsupervised Deep Learning for Structured Shape Matching

    Jean-Michel Roufosse;Abhishek Sharma;Maks Ovsjanikov

  • Exploration of continuous variability in collections of 3D shapes

    Maks Ovsjanikov;Wilmot Li;Leonidas Guibas;Niloy J. Mitra

  • Computing and processing correspondences with functional maps

    Maks Ovsjanikov;Etienne Corman;Michael Bronstein;Emanuele Rodolà

Frequent Co-Authors

Leonidas J. Guibas
Leonidas J. Guibas Stanford University
Emanuele Rodolà
Emanuele Rodolà Sapienza University of Rome
Peter Wonka
Peter Wonka King Abdullah University of Science and Technology
Michael M. Bronstein
Michael M. Bronstein University of Oxford
Niloy J. Mitra
Niloy J. Mitra University College London
Mirela Ben-Chen
Mirela Ben-Chen Technion – Israel Institute of Technology
Umberto Castellani
Umberto Castellani University of Verona
Alexander M. Bronstein
Alexander M. Bronstein Technion – Israel Institute of Technology
Michael Wand
Michael Wand Johannes Gutenberg University of Mainz
Hans-Peter Seidel
Hans-Peter Seidel Max Planck Institute for Informatics

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