The Max Planck ETH Center for Learning Systems is a joint research center of ETH Zurich and the Max Planck Society. The Center’s mission is to pursue research in the design and analysis of learning systems, synthetic or natural. This initiative brings together more than 40 professors and senior researchers in the fields of machine learning, perception, robotics on large and small scales, as well as neuroscience.
Max Planck Lecture 2017
The field of transportation is undergoing a seismic change with the coming introduction of autonomous driving. The technologies required to enable computer driven cars involves the latest cutting edge artificial intelligence algorithms along three major thrusts: Sensing, Planning and Mapping.
The German Pattern Recognition Award is awarded once a year to one young researcher in computer vision, pattern recognition or machine learning at an age of 35 years or less and sponsored by the Daimler AG with 5000€.
3D reconstruction from multiple 2D images is an inherently ill-posed problem. Prior knowledge is required to resolve ambiguities and probabilistic models are desirable to capture the ambiguities in the reconstructed model. In this talk, I will present two recent results tackling these two aspects. First, I will introduce a probabilistic framework for volumetric 3D reconstruction where the reconstruction problem is cast as inference in a Markov random field using ray potentials. Our main contribution is a discrete-continuous inference algorithm which computes marginal distributions of each voxel's occupancy and appearance. I will show that the proposed algorithm allows for Bayes optimal predictions with respect to a natural reconstruction loss. I will further demonstrate several extensions which integrate non-local CAD priors into the reconstruction process. In the second part of my talk, I will present a novel framework for deep learning with 3D data called OctNet which enables 3D CNNs on high-dimensional inputs. I will demonstrate the utility of the OctNet representation on several 3D tasks including classification, orientation estimation and point cloud labeling. Finally, I will present an extension of OctNet called OctNetFusion which jointly predicts the space partitioning function with the output representation, resulting in an end-to-end trainable model for volumetric depth map fusion.
The 2017 International conference on micro- and nanomachines will be held in Wuhan, China, from the 25-28 August and will be co-chaired by Peer Fischer
NVIDIA CEO Jensen Huang presented the NVAIL AI Labs with the very first Tesla V100 GPUs, based on NVIDIA's Volta architecture. MPI-IS is among the top centers working at the leading edge of deep learning in computer vision. As such it is recognized by NVIDIA as one of its NVAIL labs and giving the MPI access to the best and latest NVIDIA technology. Huang unveiled these new GPUs at CVPR saying that he wants to put them in the hands of researchers first.
Peer Fischer, head of the Research Group "Micro-, Nano- and Molecular Systems" at the Max Planck Institute for Intelligent Systems and Professor of Physical Chemistry at the University of Stuttgart, was awarded with a Steinhofer lecture 2017 of the University of Freiburg "for his fundamental work in the field of targeted 3D-production of artificial nanostructures and their application in biomedicine". Professor Fischer gave his lecture on "How to Teach Nanoparticles and Enzymes to Swim".
Researchers at the Max Planck Institute for Intelligent Systems (MPI-IS) have developed technology to digitally capture clothing on moving people, turn it into a 3D digital form, and dress virtual avatars with it. This new technology makes virtual clothing try-on practical.