Colloquium Details

Deep Surface Meshes

Speaker: Pascal Fua, EPFL

Location: 60 Fifth Avenue 150

Date: October 29, 2021, 11 a.m.

Host: Yann LeCun

Synopsis:

Attend via Zoom:  https://nyu.zoom.us/j/91612359757

Geometric Deep Learning has recently made striking progress with the advent of Deep Implicit Fields (SDFs). They allow for detailed modeling of watertight surfaces of arbitrary topology while not relying on a 3D Euclidean grid, resulting in a learnable 3D surface parameterization that is not limited in resolution. Unfortunately, they have not yet reached their full potential for applications that require an explicit surface representation in terms of vertices and facets because converting the SDF to such a 3D mesh representation requires a marching-cube algorithm, whose output cannot be easily differentiated with respect to the SDF parameters.

In this talk, I will our approach to overcoming this limitation and implementing convolutional neural nets that output complex 3D surface meshes while remaining fully-differentiable and end-to-end trainable. I will also present applications to single view reconstruction, physically-Driven Shape Optimization, and bio-medical image segmentation.

Speaker Bio:

Pascal Fua received an engineering degree from Ecole Polytechnique, Paris, in 1984 and the Ph.D. degree in Computer Science from the University of Orsay in 1989. He then worked at SRI International and INRIA Sophia-Antipolis as a Computer Scientist. He joined EPFL in 1996 where he is now a Professor in the School of Computer and Communication Science and heads the Computer Vision Laboratory.

His research interests include shape modeling and motion recovery from images, analysis of microscopy images, and Augmented Reality. His research interests include shape modeling and motion recovery from images, analysis of microscopy images, and machine learning. He has (co)authored over 300 publications in refereed journals and conferences. He is an IEEE Fellow and has been an Associate Editor of IEEE journal Transactions for Pattern Analysis and Machine Intelligence. He often serves as program committee member, area chair, and program chair of major vision conferences and has cofounded three spinoff companies (Pix4D, PlayfulVision, and NeuralConcept). 

Notes:

In-person attendance only available to those with active NYU ID cards. All individuals must show the Daily Screener green pass in order to gain entry to the building.


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