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.Andreas Geiger is a Max Planck Research Group Leader at the MPI for Intelligent Systems in Tbingen heading the Autonomous Vision Group (AVG), and a Visiting Professor at ETH Zrich. Prior to this, he was a research scientist in the Perceiving Systems department at MPI Tbingen. He studied at KIT, EPFL and MIT and received his PhD degree in 2013 from the Karlsruhe Institute of Technology. His research interests are at the intersection of 3D reconstruction and visual scene understanding with a particular focus on rich semantic and geometric priors for bridging the gap between low-level and high-level vision. He is particularly interested in autonomous driving applications. His work has received several prices, including the Heinz Maier Leibnitz Prize, the Ernst-Schoemperlen Award, as well as best paper awards at CVPR, GCPR and 3DV. He is an associate member of the Max Planck ETH Center for Learning Systems and serves as area chair and associate editor in computer vision (CVPR, ECCV, PAMI).