H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 32 Citations 6,200 84 World Ranking 7148 National Ranking 182

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Mathematical analysis
  • Topology

His scientific interests lie mostly in Artificial intelligence, Shape analysis, Computer vision, Invariant and Pattern recognition. His research on Artificial intelligence frequently links to adjacent areas such as Algorithm. In his study, 3d shapes, Scaling, Matrix representation and Linear algebra is inextricably linked to Active shape model, which falls within the broad field of Shape analysis.

His Invariant research includes themes of Cognitive neuroscience of visual object recognition, Homogeneous space, Topology and Image retrieval, Visual Word. His work in the fields of Pattern recognition, such as Point distribution model, overlaps with other areas such as Principal curvature. His Heat kernel signature research is multidisciplinary, incorporating elements of Scale, Heat kernel, Heat equation and Isometry.

His most cited work include:

  • A concise and provably informative multi-scale signature based on heat diffusion (1110 citations)
  • Shape google: Geometric words and expressions for invariant shape retrieval (733 citations)
  • Functional maps: a flexible representation of maps between shapes (442 citations)

What are the main themes of his work throughout his whole career to date?

Algorithm, Artificial intelligence, Shape analysis, Point cloud and Pattern recognition are his primary areas of study. His Algorithm research incorporates elements of Pointwise, Segmentation, Polygon mesh and Laplace operator. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Point, Invariant, Geodesic and Computer vision.

The Computer vision study combines topics in areas such as Training set and Inference. His Shape analysis study integrates concerns from other disciplines, such as Fixed point, Topology, Active shape model, Heat kernel signature and Eigenfunction. His studies in Pattern recognition integrate themes in fields like Range, Ground truth and Structure.

He most often published in these fields:

  • Algorithm (46.67%)
  • Artificial intelligence (43.33%)
  • Shape analysis (25.83%)

What were the highlights of his more recent work (between 2019-2021)?

  • Algorithm (46.67%)
  • Point cloud (20.83%)
  • Artificial intelligence (43.33%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Algorithm, Point cloud, Artificial intelligence, Robustness and Polygon mesh. His studies deal with areas such as Pointwise, Encoding and Laplace operator as well as Algorithm. While the research belongs to areas of Artificial intelligence, Maks Ovsjanikov spends his time largely on the problem of Pattern recognition, intersecting his research to questions surrounding Structure.

His research in Robustness intersects with topics in Geometric data analysis, Theoretical computer science, Artificial neural network, Embedding and Differentiable function. His study looks at the relationship between Artificial neural network and fields such as Outlier, as well as how they intersect with chemical problems. In his work, Surface and Existential quantification is strongly intertwined with Discretization, which is a subfield of Polygon mesh.

Between 2019 and 2021, his most popular works were:

  • PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds (44 citations)
  • Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence (12 citations)
  • PointTriNet: Learned Triangulation of 3D Point Sets (4 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Mathematical analysis
  • Topology

His primary scientific interests are in Artificial intelligence, Pattern recognition, Robustness, Point cloud and Algorithm. In his study, he carries out multidisciplinary Artificial intelligence and Functional Map research. His work on Feature learning as part of general Pattern recognition research is frequently linked to Layer, thereby connecting diverse disciplines of science.

His Robustness study also includes

  • Deep learning which is related to area like Point, Perceptron, Discretization, Theoretical computer science and Geometric data analysis,
  • Artificial neural network which is related to area like Outlier, Surface reconstruction, Noise reduction and Shape analysis. His work deals with themes such as Isometry, Polygon mesh and Consistency, which intersect with Algorithm. He has researched Polygon mesh in several fields, including Matching, Segmentation, Encoding and Laplace operator.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

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

Jian Sun;Maks Ovsjanikov;Leonidas Guibas.
symposium on geometry processing (2009)

1411 Citations

Shape google: Geometric words and expressions for invariant shape retrieval

Alexander M. Bronstein;Michael M. Bronstein;Leonidas J. Guibas;Maks Ovsjanikov.
ACM Transactions on Graphics (2011)

664 Citations

Functional maps: a flexible representation of maps between shapes

Maks Ovsjanikov;Mirela Ben-Chen;Justin Solomon;Adrian Butscher.
international conference on computer graphics and interactive techniques (2012)

570 Citations

One Point Isometric Matching with the Heat Kernel

Maks Ovsjanikov;Quentin Mérigot;Facundo Mémoli;Leonidas J. Guibas.
Computer Graphics Forum (2010)

373 Citations

Global intrinsic symmetries of shapes

Maks Ovsjanikov;Jian Sun;Leonidas Guibas.
symposium on geometry processing (2008)

280 Citations

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

Maks Ovsjanikov;Alexander M. Bronstein;Michael M. Bronstein;Leonidas J. Guibas.
international conference on computer vision (2009)

173 Citations

Dynamic geometry registration

Niloy J. Mitra;Simon Flöry;Maks Ovsjanikov;Natasha Gelfand.
symposium on geometry processing (2007)

140 Citations

Voronoi-Based Curvature and Feature Estimation from Point Clouds

Quentin Mérigot;M Ovsjanikov;L J Guibas.
IEEE Transactions on Visualization and Computer Graphics (2011)

137 Citations

Exploration of continuous variability in collections of 3D shapes

Maks Ovsjanikov;Wilmot Li;Leonidas Guibas;Niloy J. Mitra.
international conference on computer graphics and interactive techniques (2011)

134 Citations

PCPNET: Learning Local Shape Properties from Raw Point Clouds

Paul Guerrero;Yanir Kleiman;Maks Ovsjanikov;Niloy J. Mitra.
Computer Graphics Forum (2018)

133 Citations

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Best Scientists Citing Maks Ovsjanikov

Michael M. Bronstein

Michael M. Bronstein

Imperial College London

Publications: 118

Leonidas J. Guibas

Leonidas J. Guibas

Stanford University

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Alexander M. Bronstein

Alexander M. Bronstein

Technion – Israel Institute of Technology

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Emanuele Rodolà

Emanuele Rodolà

Sapienza University of Rome

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Ron Kimmel

Ron Kimmel

Technion – Israel Institute of Technology

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Daniel Cremers

Daniel Cremers

Technical University of Munich

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Niloy J. Mitra

Niloy J. Mitra

University College London

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Hao Zhang

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Simon Fraser University

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Daniel Cohen-Or

Daniel Cohen-Or

Tel Aviv University

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Hong Qin

Hong Qin

Stony Brook University

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Kai Xu

Kai Xu

National University of Defense Technology

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Yu-Kun Lai

Yu-Kun Lai

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Hans-Peter Seidel

Hans-Peter Seidel

Max Planck Institute for Informatics

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The University of Texas at Austin

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Paul L. Rosin

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