We are looking for a PhD student working on interpretable representations.
For the first time, the prestigious IEEE PAMI Young Researcher Award goes to a German researcher.
Application Deadline: Nov 15, 2018
The International Max Planck Research School for Intelligent Systems is now hiring our 2019 generation of Ph.D. students. They will contribute to world-leading research in areas such as machine learning, computer vision, robot dynamics and control, micro- and nano-robotics, and haptics.
Eshed Ohn-Bar from CMU receives Humboldt Fellowship and will join AVG in December as a post-doctoral researcher!
Am 1. März begann Geiger die Professur für "Learning-based Computer Vision" an der über 500 Jahre alten, renommierten Universität Tübingen. Geiger leitet weiterhin eine Forschungsgruppe am MPI-IS in Tübingen.
Andreas GTC Europe talk on Deep Models for 3D Reconstruction is now available.
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.
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.