On Mon September 27, 2021


Andreas Geiger (University of Tübingen / Professor, MPI for Intelligent Systems / Group Leader)


Neural Implicit Representations for 3D Vision


In this lecture, I will show recent results on learning neural implicit 3D representations, departing from the traditional paradigm of representing 3D shapes explicitly using voxels, point clouds or meshes. Neural implicit representations have a small memory footprint and allow for modeling arbitrary 3D topologies at (theoretically) arbitrary resolution in continuous function space. I will discuss the ability and limitations of these approaches in the context of reconstructing 3D geometry (Occupancy Networks), appearance (Implicit Surface Light Fields) and motion (Occupancy Flow). I will further demonstrate how implicit representations can be learned using only 2D supervision through implicit differentiation of the level set constraint (Differentiable Volumetric Rendering). Finally, I will give a brief outlook on follow-up works including NeRF, GRAF, GIRAFFE and KiloNeRF.


Andreas Geiger is a full professor at the University of Tübingen. Prior to this, he was a visiting professor at ETH Zürich and a group leader at the Max Planck Institute for Intelligent Systems. He studied at KIT, EPFL and MIT, and received his PhD degree in 2013 from the Karlsruhe Institute of Technology (KIT). His research interests are at the intersection of computer vision, machine learning and robotics, with a particular focus on 3D scene perception, deep representation learning, generative models and sensori-motor control in the context of autonomous systems. In 2012, he has published the KITTI vision benchmark suite which has become one of the most influential testbeds for evaluating stereo, optical flow, scene flow, detection, tracking, motion estimation and segmentation algorithms. His work has been recognized with several prizes, including the IEEE PAMI Young Investigator Award, the Heinz Maier Leibnitz Prize of the German Science Foundation and the German Pattern Recognition Award. In 2013 and 2021 he received the CVPR best paper and best paper runner-up awards. He also received the best paper award at GCPR 2015 and 3DV 2015 as well as the best student paper award at 3DV 2017. In 2019, he was awarded a starting grant by the European Research Council. He is a board member of the ELLIS initiative and associate faculty of the International Max Planck Research School (IMPRS) for Intelligent Systems. He coordinates the ELLIS PhD and PostDoc program. He regularly serves as area chair and associate editor for several computer vision conferences and journals including CVPR, ICCV, ECCV, PAMI and IJCV.