Seminar

Paris CG Seminar

The Telecom ParisTech Computer Graphics organizes scientific seminars on a regular basis. Please contact Tamy Boubekeur if you want to be added to the dedicated mailing list.

Next seminar

Dear colleagues,
The next Paris Computer Graphics Seminar details are below. Feel free to forward this message to your colleagues.


WHAT:

Deep Learning for 3D Surface Reconstruction

WHO:
Thibault Groueix and Pierre Alain Langlois, Ecole des Ponts ParisTech
WHEN:

Thuesday December 18th, 2pm
WHERE:
Room C48, Building C, ground floor, Telecom ParisTech, 46 rue Barrault, 75013 Paris (Metro line 5/6/7)
ABSTRACT:
The most striking successes of Convolutional Neural Networks have until now been demonstrated on images, but the world is tri-dimensional. Analyzing it should be done directly from the most informative source of data: 3D content. The development of generative methods for 3D content would also open up many applications in arts, human-machines interaction, and education. The first challenge in 3D data analysis is the choice of a data representation. Indeed, images are naturally associated to an array (pinhole model), but there is no standard representation for 3D data. Previous works in deep learning often use volumetric and point cloud representations. Volumetric representations, on the one hand, are typically memory intensive and only provide voxel-scale sampling of the underlying smooth and continuous surface. On the other hand, point clouds leverage the sparsity of the data and can provide surface details, but they lack the connectivity between the points, making it difficult to reconstruct the underlying surface with high fidelity. Surface based approaches were recently introduced and are a promising direction.


References:
  • 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction. Christopher B. Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, Silvio Savarese
  • A Point Set Generation Network for 3D Object Reconstruction from a Single Image. Haoqiang Fan, Hao Su, Leonidas Guibas
  • AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation. Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry
BIO:

Thibault Groueix has been a PhD student in the Imagine group of Ecole des Ponts under the supervision of Mathieu Aubry since December 2016. He is also working in collaboration with Adobe research, supervised by Mathew Fisher, Bryan Russel et Vova Kim. His PhD work focuses on synthesizing and analyzing 3D data with Deep Learning. In particular, the goal is to design novel methods parameterizing 3D data, and to use these learned parameterizations to reconstruct 3D content in a friendly format for computer graphics applications. Such settings include auto-encoding of 3D shapes and single-view reconstruction. In 2016, he received a Master’s degree from MVA. His past academic experiences include work on rendering in Tamy Boubekeur computer graphics group (Telecom ParisTech) and work on medical texture classification in Michael Unser group (EPFL).

Pierre Alain Langlois has been (also) a PhD student in the Imagine group of Ecole des Ponts under the supervision of Renaud Marlet since November 2017. He is also working in collaboration with Alexandre Boulch at Onera Palaiseau. His PhD’s scope is set on 3D semantic reconstruction of scenes with a particular focus on indoor/outdoor representations of buildings. In particular, this subject involves getting interested in both reconstruction methods (i.e Photogrammetry/LIDAR reconstruction) and 3D data analysis. In 2017, he received a Master’s degree from MVA. His past related work includes a project of RGBD-based object recognition (Imagine team + Pzartech Ltd.), and a multi-object tracking project (Safran Identity & Security).

Previous speakers

  • Mark Pauly, EPFL, Switzerland
  • Reinhold Preiner, TU Graz, Austria
  • Mario Botsch, Biedefeld University, Germany
  • Chris Wyman, NVidia Research, USA
  • Jean-Marc Thiery, TU Delft, Netherland
  • Rene Weller, University of Bremen, Germany
  • Ricardo Marroquim, Federal University of Rio de Janeiro, Brazil
  • Olivier Bimber, Johannes Kepler University Linz, Germany
  • Denis Zorin, New York University, USA
  • Sida Ferradans, CNRS, France
  • Gianpaolo Palma, CNR Pisa, Italy
  • Pierre Alliez, INRIA, France
  • Kenshi Takayama, National Institute of Informatics, Japan
  • Martin Hachet, INRIA France
  • Gordon Wetzstein, Massachusets Institute of Technology, USA
  • Valerio Pascucci, University of Utah, USA
  • Chuong Nguyen, Max-Planck-Institut für Informatik, Germany
  • Bruno Vallet, IGN, France
  • Karan Singh, University of Toronto, Canada
  • Alla Scheffer, University of British Columbia, Canada
  • Fredo Durant, Massachusets Institute of Technology, USA
  • Elmar Eisemann, Max-Planck-Institut für Informatik, Germany
  • Mohamed Chaouch, INRIA, France
  • Tobias Ritschel, Max Planck Institut, Germany
  • Marc Christie, IRISA, France
  • Fatemeh Abbasinejad, UC DAVIS, USA
  • Adrien Bousseau, INRIA, France