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Geometric deep learning: going beyond Euclidean data

Publications associées (73)

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3D Shape Similarity

Fan Zhang

Microsoft Kinect, Google's Project Tango and Lytro's light field camera are all examples of 3D depth sensing reaching the consumer market. As this technology becomes more widestream, new signal processing techniques are needed to exploit this data. Recent ...

2016

Dynamics of particles with anisotropic mass depending on time and position

Tudor Ratiu

Representations of solutions of equations describing the diffusion and quantum dynamics of particles in a Riemannian manifold are discussed under the assumption that the mass of particles is anisotropic and depends on both time and position. These equation ...

Springer Verlag2015

Realtime Face Tracking and Animation

Sofien Bouaziz

Capturing and processing human geometry, appearance, and motion is at the core of computer graphics, computer vision, and human-computer interaction. The high complexity of human geometry and motion dynamics, and the high sensitivity of the human visual sy ...

EPFL2015

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

ShapeOp - A Robust and Extensible Geometric Modelling Paradigm

Mark Pauly, Sofien Bouaziz, Bailin Deng, Mario Moacir Deuss, Anders Holden Deleuran

We present ShapeOp, a robust and extensible geometric modelling paradigm. ShapeOp builds on top of the state-of-the-art physics solver (Bouaziz et al. in ACM Trans Graph 33:154:1–154:11, 2014). We discuss the main theoretical advantages of the underlying s ...

Springer 2015

Self Tuning Texture Optimization

Mark Pauly, Boris Neubert, Alexandre Kaspar

The goal of example-based texture synthesis methods is to generate arbitrarily large textures from limited exemplars in order to fit the exact dimensions and resolution required for a specific modeling task. The challenge is to faithfully capture all of th ...

Wiley-Blackwell2015

Dynamic 2D/3D Registration

Mark Pauly, Sofien Bouaziz, Andrea Tagliasacchi

Image and geometry registration algorithms are an essential component of many computer graphics and computer vision systems. With recent technological advances in RGB-D sensors, such as the Microsoft Kinect or Asus Xtion Live, robust algorithms that combin ...

The Eurographics Association2014

Principal Flows

Victor Panaretos, Tung Huy Pham, Zhigang Yao

We revisit the problem of extending the notion of principal component analysis (PCA) to multivariate datasets that satisfy nonlinear constraints, therefore lying on Riemannian manifolds. Our aim is to determine curves on the manifold that retain their cano ...

American Statistical Association2014

Capturing “attrition intensifying” structural traits from didactic interaction sequences of MOOC learners

Pierre Dillenbourg, Patrick Jermann, Nan Li, Tanmay Sinha

This work is an attempt to discover hidden structural configurations in learning activity sequences of students in Massive Open Online Courses (MOOCs). Leveraging combined representations of video click- stream interactions and forum activities, we seek to ...

2014

Dynamic 2D/3D registration for the Kinect

Mark Pauly, Sofien Bouaziz

Image and geometry registration algorithms are essential components of many computer graphics and computer vision systems. With recent technological advances in RGB-D sensors, robust algorithms that combine 2D image and 3D geometry registration have become ...

ACM2013