372 citations · 641 across the 71 of their papers we have counts for
17 papers · 1 filter
Deformation Recovery: Localized Learning for Detail-Preserving Deformations
Ramana Sundararaman, Nicolas Donati, Simone Melzi +2
We introduce a novel data-driven approach aimed at designing high-quality shape deformations based on a coarse localized input signal. Unlike previous data-driven methods that requ…
Scalable and Efficient Functional Map Computations on Dense Meshes
Robin Magnet, Maks Ovsjanikov
Spectral geometric methods have brought revolutionary changes to the field of geometry processing. Of particular interest is the study of the Laplacian spectrum as a compact, isome…
Intuitive and Efficient Roof Modeling for Reconstruction and Synthesis
Jing Ren, Biao Zhang, Bojian Wu +4
We propose a novel and flexible roof modeling approach that can be used for constructing planar 3D polygon roof meshes. Our method uses a graph structure to encode roof topology an…
Learning Spectral Unions of Partial Deformable 3D Shapes
Luca Moschella, Simone Melzi, Luca Cosmo +5
Spectral geometric methods have brought revolutionary changes to the field of geometry processing. Of particular interest is the study of the Laplacian spectrum as a compact, isome…
Structured Regularization of Functional Map Computations
Jing Ren, Mikhail Panine, Peter Wonka +1
We consider the problem of non-rigid shape matching using the functional map framework. Specifically, we analyze a commonly used approach for regularizing functional maps, which co…
Wavelet-based Heat Kernel Derivatives: Towards Informative Localized Shape Analysis
M. Kirgo, S. Melzi, G. Patanè +2
In this paper, we propose a new construction for the Mexican hat wavelets on shapes with applications to partial shape matching. Our approach takes its main inspiration from the we…
MapTree: Recovering Multiple Solutions in the Space of Maps
Jing Ren, Simone Melzi, Maks Ovsjanikov +1
In this paper we propose an approach for computing multiple high-quality near-isometric dense correspondences between a pair of 3D shapes. Our method is fully automatic and does no…
Spectral Coarsening of Geometric Operators
Hsueh-Ti Derek Liu, Alec Jacobson, Maks Ovsjanikov
We introduce a novel approach to measure the behavior of a geometric operator before and after coarsening. By comparing eigenvectors of the input operator and its coarsened counter…
OperatorNet: Recovering 3D Shapes From Difference Operators
Ruqi Huang, Marie-Julie Rakotosaona, Panos Achlioptas +2
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 n…
ZoomOut: Spectral Upsampling for Efficient Shape Correspondence
Simone Melzi, Jing Ren, Emanuele Rodolà +3
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 mai…
PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds
Marie-Julie Rakotosaona, Vittorio La Barbera, Paul Guerrero +2
Point clouds obtained with 3D scanners or by image-based reconstruction techniques are often corrupted with significant amount of noise and outliers. Traditional methods for point…
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,…
Multi-directional Geodesic Neural Networks via Equivariant Convolution
Adrien Poulenard, Maks Ovsjanikov
We propose a novel approach for performing convolution of signals on curved surfaces and show its utility in a variety of geometric deep learning applications. Key to our construct…
Latent Space Representation for Shape Analysis and Learning
Ruqi Huang, Panos Achlioptas, Leonidas Guibas +1
We propose a novel shape representation useful for analyzing and processing shape collections, as well for a variety of learning and inference tasks. Unlike most approaches that ca…
Continuous and Orientation-preserving Correspondences via Functional Maps
Jing Ren, Adrien Poulenard, Peter Wonka +1
We propose a method for efficiently computing orientation-preserving and approximately continuous correspondences between non-rigid shapes, using the functional maps framework. We…
Robust Structure-based Shape Correspondence
Yanir Kleiman, 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…
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…