You can also find most up-to-date entries for my latest publications on the Google Scholar page.

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Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence
Nicolas Donati, Abhishek Sharma, Maks Ovsjanikov

We present a novel learning-based approach for computing correspondences between non-rigid 3D shapes that is both accurate and robust to changes in shape structure. Key to our method is a feature-extraction network that learns directly from raw shape geometry, combined... oral presentation (5% acceptance rate)

Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020

[paper] [code demo]

Spectral Mesh Simplification
Thibault Lescoat, Hsueh-Ti Derek Liu, Jean-Marc Thiery, Alec Jacobson, Tamy Boubekeur, Maks Ovsjanikov

We propose a spectrum-preserving mesh decimation scheme. Given an input mesh we compute a simplified triangle mesh such that its Laplacian is spectrally close to the one of the input. We illustrate the benefit of our approach for quickly approximating...

Proc. Eurographics, 2020

[paper] [compressed [7MB]] [code demo]


ZoomOut: Spectral Upsampling for Efficient Shape Correspondence
Simone Melzi, Jing Ren, Emanuele Rodolà, Abhishek Sharma, Peter Wonka, Maks Ovsjanikov

We present a simple and efficient method for refining maps or correspondences by iterative upsampling in the spectral domain that can be implemented in a few lines of code. Our main observation is that high quality maps can be obtained...

Transactions on Graphics (Proc. SIGGRAPH Asia), 2019

[paper [50MB]] [compressed [3MB]] [supplementary materials] [code demo]

Unsupervised Deep Learning for Structured Shape Matching
Jean-Michel Roufosse, Abhishek Sharma, Maks Ovsjanikov

We present a novel method for computing correspondences across 3D shapes using unsupervised learning. Our method computes a non-linear transformation of given descriptor functions, while optimizing for global structural properties of the resulting maps, such as their bijectivity or approximate... oral presentation, (4.6% acceptance rate) Best Paper Nomination (one of 7, out of 4303 submissions).

Proc. International Conference on Computer Vision (ICCV), 2019

[paper] [code demo]

OperatorNet: Recovering 3D Shapes From Difference Operators
Ruqi Huang, Marie-Julie Rakotosaona, Panos Achlioptas, Leonidas Guibas, Maks Ovsjanikov

This paper proposes a learning-based framework for reconstructing 3D shapes from functional operators, compactly encoded as small-sized matrices. To this end we introduce a novel neural architecture, called OperatorNet, which takes as input a set of linear operators representing a...

Proc. International Conference on Computer Vision (ICCV), 2019

[paper] [code demo]

Effective Rotation-invariant Point CNN with Spherical Harmonics Kernels
Adrien Poulenard, Marie-Julie Rakotosaona, Yann Ponty, Maks Ovsjanikov

We present a novel rotation invariant architecture operating directly on point cloud data. We demonstrate how rotation invariance can be injected into a recently proposed point-based PCNN architecture, on all layers of the network. This leads to invariance to both... oral presentation

Proc. International Conference on 3D Vision (3DV), 2019

[paper] [code demo]

Correspondence-Free Region Localization for Partial Shape Similarity via Hamiltonian Spectrum Alignment
Arianna Rampini, Irene Tallini, Maks Ovsjanikov, Alex M. Bronstein, Emanuele Rodolà

We consider the problem of localizing relevant subsets of non-rigid geometric shapes given only a partial 3D query as the input. Such problems arise in several challenging tasks in 3D vision and graphics, including partial shape similarity, retrieval, and non-rigid... oral presentation. Best Paper Award

Proc. International Conference on 3D Vision (3DV), 2019


POINTCLEANNET: Learning to Denoise and Remove Outliers from Dense Point Clouds
Marie-Julie Rakotosaona, Vittorio La Barbera, Paul Guerrero, Niloy J. Mitra, Maks Ovsjanikov

We propose a simple data-driven method for removing outliers and reducing noise in unordered point clouds. We base our approach on a deep learning architecture adapted from PCPNet. In our extensive evaluation, both on synthetic and real data, we show...

Computer Graphics Forum, 2019

[paper [56MB]] [compressed [9MB]] [code demo] [website with data and models]

Connectivity-preserving Smooth Surface Filling with Sharp Features
Thibault Lescoat, Pooran Memari, Jean-Marc Thiery, Maks Ovsjanikov, Tamy Boubekeur

We present a method for constructing a surface mesh filling gaps between the boundaries of multiple disconnected input components. Unlike previous works, our method pays special attention to preserving both the connectivity and large-scale geometric features of input parts, while... short paper

Computer Graphics Forum (Proc. Pacific Graphics), 2019


Structured Regularization of Functional Map Computations
Jing Ren, Mikhail Panine, Peter Wonka, Maks Ovsjanikov

We analyze a commonly used approach for regularizing functional maps, via commutativity with the Laplace-Beltrami operators and show that it has certain fundamental theoretical limitations. Instead we propose a novel, theoretically well-justified approach for regularizing functional maps, by using the... Best Paper Award honorable mention (one of three)

Computer Graphics Forum (Proc. SGP), 2019

[paper [23MB]] [compressed [3MB]] [code demo]

Limit Shapes – A Tool for Understanding Shape Differences and Variability in 3D Model Collections
Ruqi Huang, Panos Achlioptas, Leonidas Guibas, Maks Ovsjanikov

We propose a method for extracting a central or limit shape in a collection, connected via a functional map network. Our approach is based on enriching the latent space induced by a functional map network with an additional natural metric...

Computer Graphics Forum (Proc. SGP), 2019

[paper [29MB]] [compressed [4MB]]

Spectral Coarsening of Geometric Operators
Hsueh-Ti Derek Liu, Alec Jacobson, Maks Ovsjanikov

We show that it is possible to significantly reduce the sampling density of an operator derived from a 3D shape without affecting the low-frequency eigenvectors. For this, we first introduce a novel approach to measure the behavior of a geometric...

Transactions on Graphics (Proc. SIGGRAPH), 2019

[paper [58MB]] [compressed [6MB]] [code demo]

Isospectralization, or how to hear shape, style, and correspondence
Luca Cosmo, Mikhail Panine, Arianna Rampini, Maks Ovsjanikov, Michael Bronstein, Emanuele Rodolà

The question whether one can recover the shape of a geometric object from its Laplacian spectrum (‘hear the shape of the drum’) is a classical problem in spectral geometry with a broad range of implications and applications. While theoretically the...

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019

[paper] [code demo]

Functional Characterization of Deformation Fields
Etienne Corman, Maks Ovsjanikov

In this paper we present a novel representation for deformation fields of 3D shapes, by considering the induced changes in the underlying metric. In particular, our approach allows to represent a deformation field in a coordinate-free way as a linear...

Transactions on Graphics (to be presented at SIGGRAPH), 2019

[paper] [supplementary material]


Multi-directional Geodesic Neural Networks via Equivariant Convolution
Adrien Poulenard, Maks Ovsjanikov

We propose an approach for performing convolution of signals on curved surfaces and show its utility in a variety of geometric deep learning applications. Key to our construction is the notion of directional functions defined on the surface which can...

Transactions on Graphics (Proc. SIGGRAPH Asia), 2018

[paper] [sample code]

Continuous and Orientation-preserving Correspondences via Functional Maps
Jing Ren, Adrien Poulenard, Peter Wonka, Maks Ovsjanikov

We propose a method for efficiently computing orientation-preserving and approximately continuous correspondences between non-rigid shapes, using the functional maps framework. We first show how orientation preservation can be formulated directly in the functional (spectral) domain without using landmark or region...

Transactions on Graphics (Proc. SIGGRAPH Asia), 2018

[paper] [sample code]

Spectral Measures of Distortion for Change Detection in Dynamic Graphs
Luca Castelli Aleardi, Semih Salihoglu, Gurprit Singh, Maks Ovsjanikov

We propose a novel framework for detecting, quantifying and visualizing changes between two snapshots of a dynamic network. We show how a theoretically-justified, inherently multi-scale notion of change, or distortion, can be defined and computed using spectral graph-theoretic tools. Our...

Complex Networks, 2018

[preprint] [extended version]

Topological Function Optimization for Continuous Shape Matching
Adrien Poulenard, Primoz Skraba, Maks Ovsjanikov

We present an approach for optimizing real-valued functions based on a wide range of topological criteria. Our method is based on using persistence diagrams associated with real-valued functions, and on the analysis of the derivatives of these diagrams with respect...

Computer Graphics Forum (Proc. SGP), 2018

[paper] [sample code]

A Survey on Data-driven Dictionary-based Methods for 3D Modeling
Thibault Lescoat, Maks Ovsjanikov, Pooran Memari, Jean-Marc Thiery, Tamy Boubekeur

In this survey, we provide an overview of data-driven dictionary-based methods in geometric modeling. We structure our discussion by application domain: surface reconstruction, compression, and synthesis. Contrary to previous surveys, we place special emphasis on dictionary-based methods suitable for 3D...

Computer Graphics Forum (Eurographics State-of-the-Art Report), 2018

[survey paper] [website]

PCPNET Learning Local Shape Properties from Raw Point Clouds
Paul Guerrero, Yanir Kleiman, Maks Ovsjanikov, Niloy J. Mitra

We propose a deep-learning based approach for estimating local 3D shape properties in point clouds. Our approach is especially well-adapted for estimating local shape properties such as normals (both unoriented and oriented) and curvature from raw point clouds in the...

Computer Graphics Forum (Proc. Eurographics), 2018

[paper] [code and data]

Improved Functional Mappings via Product Preservation
Dorian Nogneng*, Simone Melzi*, Emanuele Rodolà, Umberto Castellani, Michael Bronstein and Maks Ovsjanikov

We consider the problem of information transfer across shapes and propose an extension to the functional map representation. Our main observation is that the functional algebra (i.e., the ability to take pointwise products of functions) can significantly extend the power... *equal contribution

Computer Graphics Forum (Proc. Eurographics), 2018

[paper] [supplementary material] [code demo]

Robust Structure-based Shape Correspondence
Yanir Kleiman and Maks Ovsjanikov

We present a robust method to find region-level correspondences between shapes, which are invariant to changes in geometry and applicable across multiple shape representations. We generate simplified shape graphs by jointly decomposing the shapes, and devise an adapted graph-matching technique,...

Computer Graphics Forum, 2018

[paper] [sample code]

Discrete Time Evolution Process Descriptor for Shape Analysis and Matching
Simone Melzi, Maks Ovsjanikov, Giorgio Roffo, Marco Cristani and Umberto Castellani

We propose a theoretically sound and efficient approach for the simulation of a discrete time evolution process that runs through all possible paths between pairs of points on a surface represented as a triangle mesh in the discrete setting. We...

Transactions on Graphics (presented at SIGGRAPH), 2018

[paper] [sample code]


Region-based Correspondence Between 3D Shapes via Spatially Smooth Biclustering
Matteo Denitto, Simone Melzi, Manuele Bicego, Umberto Castellani, Alessandro Farinelli, Mário A. T. Figueiredo, Yanir Kleiman, Maks Ovsjanikov

We exploit the connection between biclustering and matching and use it to develop a new region-based correspondence algorithm, which: (i) casts the problem from a probabilistic low-rank matrix factorization perspective; (ii) uses a spike and slab prior to induce sparsity;...

International Conference on Computer Vision (ICCV), 2017

[paper] [poster] [code demo]

Joint Graph Layouts for Visualizing Collections of Segmented Meshes
Jing Ren, Jens Schneider, Maks Ovsjanikov and Peter Wonka

We present a novel approach for computing joint graph layouts and use it to visualize collections of segmented meshes. Our algorithm takes as input the set of graphs along with partial, possibly soft, correspondences between them and constructs a consistent...

Transactions on Visualization and Computer Graphics (TVCG), 2017

[paper] [video] [code demo]

Adjoint Map Representation for Shape Analysis and Matching
Ruqi Huang and Maks Ovsjanikov

We propose to consider the adjoint operators of functional maps and demonstrate their utility in several tasks in geometry processing. Unlike a functional map, the adjoint operator reflects both the map and its distortion. We show that this property of... Awarded the Replicability Stamp

Computer Graphics Forum (proc. Symposium on Geometry Processing), 2017

[paper] [code]

On the Stability of Functional Maps and Shape Difference Operators
Ruqi Huang, Frederic Chazal and Maks Ovsjanikov

In this paper, we provide stability guarantees for two frameworks that are based on the notion of functional maps - the shape difference operators and the techniques used to analyze and visualize the deformations between shapes. In theory, we formulate...

Computer Graphics Forum, 2017


Consistent Functional Cross Field Design for Mesh Quadrangulation
Omri Azencot, Etienne Corman, Mirela Ben-Chen and Maks Ovsjanikov

We propose a novel technique for computing consistent cross fields on a pair of triangle meshes given an input correspondence, which we use as guiding fields for approximately consistent quadrangulations. Unlike the majority of existing methods our approach does not...

Transactions on Graphics (Proc. SIGGRAPH), 2017


A Functional View of Geometry Processing (Operator-based Techniques for Shape Analysis)
Maks Ovsjanikov

This habilitation dissertation describes a set of approaches for analyzing and processing 3D shapes and their relations. The main unifying theme of this work is the observation that many concepts in geometric data analysis can be considered, both in theory...

Habilitation à diriger des recherches (Université Paris-Sud), 2017

[Habilitation thesis]

Informative Descriptor Preservation via Commutativity for Shape Matching
Dorian Nogneng and Maks Ovsjanikov

We show that considering descriptors as linear operators acting on functions through multiplication, rather than as simple scalar-valued signals, allows to extract significantly more information from a given descriptor and ultimately results in a more accurate map estimation. Namely, we...

Computer Graphics Forum (Proc. Eurographics), 2017

[paper] [code demo]

Functional Characterization of Intrinsic and Extrinsic Geometry
Etienne Corman*, Justin Solomon*, Mirela Ben-Chen, Leonidas Guibas, Maks Ovsjanikov

We propose a novel way to capture and characterize distortion between shapes, by first showing that the discrete metric can be recovered from the previously proposed shape difference operators, by solving two linear systems of equations. We then introduce an... *equal contribution

ACM Transactions on Graphics (TOG), presented at SIGGRAPH, 2017


Computing and Processing Correspondences with Functional Maps
Maks Ovsjanikov, Etienne Corman, Michael Bronstein, Emanuele Rodolà, Mirela Ben-Chen, Leonidas Guibas, Frederic Chazal, Alex Bronstein

These course notes introduce the audience to the techniques for computing and processing correspondences between geometric objects, such as 3D shapes, images or point clouds based on the functional map framework. We provide the mathematical background, computational methods and various...

Proc. SIGGRAPH courses, 2017

[SIGGRAPH 2017 course notes] [SIGGRAPH 2017 course website] [SIGGRAPH Asia 2016 course website]


Scene Structure Inference through Scene Map Estimation
Moos Hueting, Viorica Pătrăucean, Maks Ovsjanikov, Niloy Mitra

In this paper, we propose the concept of a scene map, a coarse scene representation, which describes the locations of the objects present in the scene from a top down view, as well as a pipeline to extract such a...

Proc. VMV - Vision, Modeling and Visualization, 2016

[paper] [webpage] [code]

Stable Region Correspondences Between Non-Isometric Shapes
Vignesh Ganapathi-Subramanian, Boris Thibert, Maks Ovsjanikov, Leonidas Guibas

We propose a method to build correspondences between regions or parts of 3D models that are related but not necessarily very similar. We first build an affinity matrix between points on the two shapes, based on feature rank similarity over...

Computer Graphics Forum (proc. Symposium on Geometry Processing), 2016

[paper] [code demo]

Persistence-based Pooling for Shape Pose Recognition,
Thomas Bonis, Maks Ovsjanikov, Steve Oudot and Frederic Chazal

We propose a novel pooling approach for shape classification and recognition using the bag-of-words pipeline, based on topological persistence. Our technique extends standard max-pooling and provides significantly more informative and spatially sensitive characterizations of the feature functions.

International Workshop on Computational Topology in Image Context, 2016



CROSSLINK: Joint Understanding of Image and 3D Model Collections through Shape and Camera Pose Variations
Moos Hueting, Maks Ovsjanikov, Niloy Mitra

We introduce a system for joint image-3D model processing that uses the complementary strengths of each data modality to facilitate analysis and exploration. We use it to improve the quality of text-based 3D model search, align the filtered 3D model...

Transactions on Graphics (Proc. SIGGRAPH Asia), 2015

[paper] [video] [webpage] [code]

Stable Topological Signatures for Points on 3D Shapes
Mathieu Carrière, Steve Oudot and Maks Ovsjanikov

In this paper, we propose the first point descriptor that captures the topological structure of a shape as "seen" from a single point, in a multiscale and provably stable way. We also demonstrate how a large class of topological signatures,...

Computer Graphics Forum (Proc. Symposium on Geometry Processing), 2015


Continuous Matching via Vector Field Flow
Etienne Corman, Maks Ovsjanikov and Antonin Chambolle

We present a new method for non-rigid shape matching designed to enforce continuity of the resulting correspondence. In particular, starting from an arbitrary continuous map between two surfaces we find an optimal flow that makes the final correspondence operator as...

Computer Graphics Forum (Proc. Symposium on Geometry Processing), 2015

[paper] [code]

Efficient and practical tree preconditioning for solving Laplacian systems
Luca Castelli Aleardi, Alexandre Nolin and Maks Ovsjanikov

We consider the problem of designing efficient iterative methods for solving linear systems, associated with Laplacian matrices of undirected graphs. In particular, we study a class of preconditioners, known as tree preconditioners, introduced by Vaidya, and propose a very simple...

Proc. International Symposium on Experimental Algorithms (SEA), 2015

[paper] [code]

Discrete Derivatives of Vector Fields on Surfaces — An Operator Approach
Omri Azencot, Maks Ovsjanikov, Frederic Chazal and Mirela Ben-Chen

We consider the problem of computing the Levi-Civita covariant derivative on triangle meshes, and provide a simple, easy-to-implement discretization for which we demonstrate experimental convergence. We also formally show the imposibility of satisfying all of the properties of Levi-Civita connection...

Transactions on Graphics (TOG), presented at SIGGRAPH, 2015

[paper] [supplementary material]

Affine-Invariant Visual Phrases for Object Instance Recognition
Viorica Pătrăucean and Maks Ovsjanikov

In this paper, we propose a simple and efficient scheme for object instance recognition that can cover a of full affine transformations. Remarkably, although 3 feature pairs are necessary to define a unique affine transformation, we show how a quadratic...

IAPR International Conference on Machine Vision Applications (MVA), 2015



Supervised Descriptor Learning for Non-Rigid Shape Matching
Etienne Corman, Maks Ovsjanikov, Antonin Chambolle

We present a novel method for computing correspondences between pairs of non-rigid shapes. Unlike the majority of existing techniques that assume a deformation model, we consider the problem of learning a correspondence model given a collection of reference pairs with...

Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment (NORDIA), Proc. ECCV, 2014

[paper] [code]

Functional Fluids on Surfaces
Omri Azencot, Steffen Weißmann, Maks Ovsjanikov, Max Wardetzky, Mirela Ben-Chen

We consider the problem of simulating the behavior of an incompressible fluid on a curved surface represented as an unstructured triangle mesh. We propose to model fluids using their vorticity, i.e., by a (time-varying) scalar function on the surface. The...

Computer Graphics Forum (Proc. SGP), 2014

[paper] [video]

Unsupervised Multi-Class Joint Image Segmentation
Fan Wang, Qixing Huang, Maks Ovsjanikov, Leonidas Guibas

In this paper, we present a method to jointly segment a set of images containing objects from multiple classes. We first establish consistent functional maps across the input images, and introduce a formulation that explicitly models partial similarity across images...

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014


Persistence-based Structural Recognition
Chunyuan Li, Maks Ovsjanikov and Frederic Chazal

We demonstrate that persistence diagrams built from functions defined on the objects can serve as compact and informative descriptors for images and shapes. Extensive experiments on 3D shape retrieval, hand gesture recognition and texture classification demonstrate that this approach can...

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014



Detection of Mirror-Symmetric Image Patches
Viorica Pătrăucean, Rafael Grompone von Gioi, Maks Ovsjanikov

We propose a novel approach for detecting partial reflectional symmetry in images. Our method uses a principled statistical procedure inspired from the a contrario theory to validate potential symmetries by minimizing the number of false positives.

Workshop on Symmetry Detection from Real World Images, Proc. CVPR, 2013


An Operator Approach to Tangent Vector Field Processing
Omri Azencot, Mirela Ben-Chen, Frederic Chazal, Maks Ovsjanikov

We introduce a novel coordinate-free method for manipulating and analyzing vector fields on discrete surfaces. Unlike the commonly used representations of a vector field as an assignment of vectors to the faces of the mesh, or as real values on...

Computer Graphics Forum (Proc. of SGP), 2013

[paper] [code]

Shape Matching via Quotient Spaces
Maks Ovsjanikov, Quentin Mérigot, Viorica Pătrăucean, Leonidas Guibas

In this paper, we consider the symmetric ambiguity problem present when matching shapes with intrinsic symmetries. Instead of sampling landmark correspondences, we address this problem directly by performing shape matching in an appropriate quotient space, where the symmetry has been...

Computer Graphics Forum (Proc. of SGP), 2013


Map-Based Exploration of Intrinsic Shape Differences and Variability
Raif Rustamov, Maks Ovsjanikov, Omri Azencot, Mirela Ben-Chen, Frederic Chazal, Leonidas Guibas

We develop a novel formulation for the notion of shape differences, aimed at providing detailed information about the location and nature of the differences or distortions between the two shapes being compared. Our difference operator, derived from a shape map....

ACM Transactions on Graphics (Proc. SIGGRAPH), 2013


Analysis and Visualization of Maps Between Shapes
Maks Ovsjanikov, Mirela Ben-Chen, Frederic Chazal and Leonidas Guibas

In this paper we propose a method for analysing and visualizing individual maps between shapes, or collections of such maps. Unlike the majority of prior work, which focuses on discovering maps in the context of shape matching, our main focus...

Computer Graphics Forum (CGF), 2013



Functional Maps: A Flexible Representation of Maps Between Shapes
Maks Ovsjanikov, Mirela Ben-Chen, Justin Solomon, Adrian Butscher and Leonidas Guibas

We present a novel representation of maps between pairs of shapes that allows for efficient inference and manipulation. Key to our approach is a generalization of the notion of map that puts in correspondence real-valued functions rather than points on...

ACM Transactions on Graphics (Proc. SIGGRAPH), 2012

[paper] [code]


Condition Number for Non-Rigid Shape Matching
Maks Ovsjanikov, Qi-xing Huang and Leonidas Guibas

In this paper, we study the hardness of the problem of shape matching, and introduce the notion of the shape condition number, which captures the intuition that some shapes are inherently more difficult to match against than others, depending on...

Comput. Graph. Forum (Proc. SGP), 2011


Exploration of Continuous Variability in Collections of 3D Shapes
Maks Ovsjanikov, Wilmot Li, Leonidas Guibas and Niloy Mitra

We present a method for discovering and exploring continuous variability in a collection of 3D shapes without correspondences. Our method is based on a novel navigation interface that allows users to explore a collection of related shapes by deforming a...

ACM Transactions on Graphics (Proc. SIGGRAPH), 2011

[paper] [video]

Shape Google: Geometric words and expressions for invariant shape retrieval
Alexander Bronstein, Michael Bronstein, Leonidas Guibas and Maks Ovsjanikov

In this article, we bring the spirit of feature-based computer vision approaches to the problem of nonrigid shape search and retrieval. Using multiscale diffusion heat kernels as “geometric words,” we construct compact and informative shape descriptors.

ACM Transactions on Graphics (presented at SIGGRAPH), 2011

[paper] [code]


Voronoi-based Curvature and Feature Estimation from Point Clouds
Quentin Mérigot, Maks Ovsjanikov and Leonidas Guibas

We present an efficient and robust method for extracting curvature information, sharp features and normal directions of a piecewise smooth surface from its point cloud sampling in a unified framework.

IEEE Transactions On Visualisation and Computer Graphics (TVCG), 2010

[paper] [code]

Topic Modeling for Personalized Recommendation of Volatile Items
Maks Ovsjanikov and Ye Chen

We propose an efficient topic modeling framework in the presence of volatile dyadic observations when direct topic modeling is infeasible. We show both theoretically and empirically that often-available unstructured and semantically-rich meta-data can serve as a…

Proc. European Conference on Machine Learning (ECML/PKDD), 2010


One Point Isometric Matching with the Heat Kernel
Maks Ovsjanikov, Quentin Mérigot, Facundo Mémoli and Leonidas Guibas

In this paper, we show that under mild genericity conditions, a single correspondence can be used to recover an isometry defined on entire shapes, and thus the space of all isometries can be parameterized by one correspondence between a pair...

Computer Graphics Forum (Proc. SGP), 2010


Persistence-based Segmentation of Deformable Shapes
Primoz Skraba, Maks Ovsjanikov, Frederic Chazal and Leonidas Guibas

In this paper, we combine two ideas: persistence-based clustering and the Heat Kernel Signature (HKS) function to obtain a multi-scale isometry invariant mesh segmentation algorithm. The key advantages of this approach is that it is tunable through a few intuitive... Best Paper Award

NORDIA Workshop, Proc. CVPR, 2010

[paper] [code demo]

SHREC 2010: robust feature detection and description benchmark
Alexander Bronstein, Michael Bronstein, Benjamin Bustos et al.

The benchmark tests the performance of shape feature detectors and descriptors under a wide variety of transformations.

Proc. Eurographics 2010 Workshop on 3D Object Retrieval (3DOR), 2010


Image Webs: Computing and Exploiting Connectivity in Image Collections
Kyle Heath, Natasha Gelfand, Maks Ovsjanikov, Mridul Aanjaneya, Leonidas Guibas

The focus of this paper is to study the large-scale connectivity of image collections and the use of this connectivity in a variety of applications.

Proc. Conference on Computer Vision and Pattern Recognition (CVPR), 2010


Meshless Shape and Motion Design for Multiple Deformable Objects
Bart Adams, Martin Wicke, Maks Ovsjanikov, Michael Wand, Hans-Peter Seidel and Leonidas Guibas

We present physically based algorithms for interactive deformable shape and motion modeling. We coarsely sample the objects with simulation nodes, and apply a meshless finite element method to obtain realistic deformations.

Computer Graphics Forum (CGF), 2010


ShapeGoogle: a computer vision approach for invariant shape retrieval
Maks Ovsjanikov, Alexander Bronstein, Michael Bronstein and Leonidas Guibas

Feature-based methods have recently gained popularity in computer vision and pattern recognition communities. In this paper, we explore analogous approaches in the 3D world applied to the problem of non-rigid shape search and retrieval in large databases.

NORDIA Workshop, Proc. ICCV, 2010

[paper] [related code]


Robust Voronoi-based Curvature and Feature Estimation
Quentin Mérigot, Maks Ovsjanikov and Leonidas Guibas

We present an efficient and robust method for extracting principal curvatures, sharp features and normal directions of a piecewise smooth surface from its point cloud sampling, with theoretical guarantees. Best Paper Award

Proc. SIAM/ACM, GD/SPM, 2009

[paper] [code]

A Concise and Provably Informative Multi-scale Signature Based on Heat Diffusion
Jian Sun, Maks Ovsjanikov and Leonidas Guibas

We propose a novel point signature based on the properties of the heat diffusion process on a shape. Our signature, called the Heat Kernel Signature (or HKS), is obtained by restricting the well-known heat kernel to the temporal. Best Paper Award

Proc. Symposium on Geometry Processing (SGP), 2009


Efficient Reconstruction of Non-rigid Shape and Motion from Real-Time 3D Scanner Data
Michael Wand, Bart Adams, Maks Ovsjanikov, Alexander Berner, Martin Bokeloh, Philipp Jenke, Leonidas Guibas, Hans-Peter Seidel, Andreas Schilling

We present a new technique for reconstructing a single shape and its non-rigid motion from 3D scanning data. Our algorithm takes a set of time-varying sample points that show partial views of a deforming object.

ACM Transactions on Graphics (TOG), 2009



Global Intrinsic Symmetries of Shapes
Maks Ovsjanikov, Jian Sun and Leonidas Guibas

In this paper, we present a novel approach for efficiently computing symmetries of a shape which are invariant up to isometry preserving transformations. We show that the intrinsic symmetries are transformed into the Euclidean. Best Student Paper Award

Computer Graphics Forum (Proc. SGP), 2008


Meshless Modeling of Deformable Shapes and their Motion
Bart Adams, Maks Ovsjanikov, Michael Wand, Hans-Peter Seidel and Leonidas J. Guibas

We present a new framework for interactive shape deformation modeling and key frame interpolation based on a meshless finite element formulation. We formulate rigidity and volume preservation constraints.

Proc. ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA), 2008

[paper] [video]


Dynamic Geometry Registration
Niloy J. Mitra, Simon Floery, Maks Ovsjanikov, Natasha Gelfand, Leonidas Guibas and Helmut Pottmann

We propose an algorithm that performs registration of large sets of unstructured point clouds of moving and deforming objects without computing correspondences. Given as input a set of frames with dense spatial.

Proc. Eurographics Symposium on Geometry Processing (SGP), 2007