Shape reconstruction is one of the fundamental problems in computer vision. It can be formulated as an inverse problem given a collection of (2D) observations. Deep neural networks excel in these kind of settings, however, they do not work well with many common 3D shape discretizations, e.g. triangular meshes. In this project we will analyze which shape representations and neural network architectures benefit from one another, and develop new algorithms that allow fast, accurate reconstructions using deep learning.
Prof. Dr. Gitta Kutyniok +
Prof. Dr. Daniel Cremers +
University: TU München, Department of Computer Science, Informatik 9, 02.09.054
Address: Boltzmannstrasse 3, 85748 Garching, GERMANY
Tel: +49 89 28917755
Fax: +49 89 28917757