Publications by Pierre Vandergheynst

Graph-based denoising for time-varying point clouds

Pierre Vandergheynst, Johann Paratte, Yann Mikaël Schoenenberger

Noisy 3D point clouds arise in many applications. They may be due to errors when creating a 3D model from images or simply to imprecise depth sensors. Point clouds can be given geometrical structure using graphs created from the similarity information betw ...

2015

Compressive image acquisition in modern CMOS IC design

Pierre Vandergheynst, Alexandre Schmid, Yusuf Leblebici, Mahdad Hosseini Kamal, Nikola Katic

Compressive sampling (CS) offers bandwidth, power, and memory size reduction compared to conventional (Nyquist) sampling. These are very attractive features for the design of modern complementary metal-oxide semiconductor (CMOS) image sensors, cameras, and ...

Wiley-Blackwell2015

Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling

k

-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, \ie, independent of the graph structure, and its perform ...

2015

MATHICSE Technical Report : Accelerated filtering on graphs using Lanczos method

Pierre Vandergheynst, Daniel Kressner, Nathanaël Perraudin, Ana Susnjara

Sinal-processing on graphs has developed into a very active field of research during the last decade. In particular, the number of applications using frames con-structed from graphs, like wavelets on graphs, has substantially increased. To attain scalabili ...

MATHICSE2015

Spectrum-Adapted Tight Graph Wavelet and Vertex-Frequency Frames

Pierre Vandergheynst, David Shuman

We consider the problem of designing spectral graph filters for the construction of dictionaries of atoms that can be used to efficiently represent signals residing on weighted graphs. While the filters used in previous spectral graph wavelet constructions ...

Institute of Electrical and Electronics Engineers2015

Enhanced Matrix Completion with Manifold Learning

Pierre Vandergheynst, Michael Bronstein, Xavier Bresson, Vasilis Kalofolias

We study the problem of matrix completion when infor- mation about row or column proximities is available, in the form of weighted graphs. The problem can be formulated as the optimization of a convex function that can be solved efficiently using the alter ...

2015

ShapeNet: Convolutional Neural Networks on Non-Euclidean Manifolds

Pierre Vandergheynst, Michael Bronstein

Feature descriptors play a crucial role in a wide range of geometry analysis and processing applications, including shape correspondence, retrieval, and segmentation. In this paper, we propose ShapeNet, a generalization of the popular convolutional neural ...

2015

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Pierre Vandergheynst, Mahdad Hosseini Kamal

We present a novel approach to the reconstruction of depth from light field data. Our method uses dictionary representations and group sparsity constraints to derive a convex formulation. Although our solution results in an increase of the problem dimensio ...

Springer-Verlag Berlin2015

Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks

Pierre Vandergheynst, Michael Bronstein

In this paper, we propose a generalization of convolutional neural networks (CNN) to non-Euclidean domains for the analysis of deformable shapes. Our construction is based on localized frequency analysis (a generalization of the windowed Fourier transform ...

Wiley-Blackwell2015

Ultra low power design of hardware efficient CS-Based compression scheme in WBSN

David Atienza Alonso, Pierre Vandergheynst, Hossein Mamaghanian

Ultra-low power optimization is a challenging research topic in the de- sign of the sensor front-ends, especially in the area of Wireless Body Sensor Nodes (WBSN), where a limited amount of power and hardware resources are available. In this talk, I analyz ...

2014

Pierre Vandergheynst

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Graph-based denoising for time-varying point clouds

Compressive image acquisition in modern CMOS IC design

Random sampling of bandlimited signals on graphs

MATHICSE Technical Report : Accelerated filtering on graphs using Lanczos method

Spectrum-Adapted Tight Graph Wavelet and Vertex-Frequency Frames

Enhanced Matrix Completion with Manifold Learning

ShapeNet: Convolutional Neural Networks on Non-Euclidean Manifolds

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks

Ultra low power design of hardware efficient CS-Based compression scheme in WBSN

Compressive image acquisition in modern CMOS IC design

Spectrum-Adapted Tight Graph Wavelet and Vertex-Frequency Frames

Graph-based denoising for time-varying point clouds

MATHICSE Technical Report : Accelerated filtering on graphs using Lanczos method

ShapeNet: Convolutional Neural Networks on Non-Euclidean Manifolds

Enhanced Matrix Completion with Manifold Learning

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Random sampling of bandlimited signals on graphs

Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks

Ultra low power design of hardware efficient CS-Based compression scheme in WBSN