Learning to Reconstruct 3D Objects

May 21, 2019 at 3:30 pm by

Place: Large Lecture Room


I will talk about two recent results on learning-based 3D reconstruction. First, I will introduce Occupancy Networks as a new representation for learning-based 3D reconstruction. Occupancy networks implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier. In contrast to existing approaches, this representation encodes a description of the 3D output at infinite resolution without excessive memory footprint. Second, I will present a learning-based solution to abstracting complex 3D shapes. In particular, I will demonstrate that superquadrics lead to more expressive 3D scene parses while being easier to learn than 3D cuboid representations. The proposed model learns to parse 3D objects into consistent superquadric representations without supervision. Results on various ShapeNet categories as well as the SURREAL human body dataset demonstrate the flexibility of the model in capturing fine details and complex poses.

Short Bio:

Andreas Geiger is a full professor at the University of Tübingen and a group leader at the Max Planck Institute for Intelligent Systems. Prior to this, he was a visiting professor at ETH Zürich and a research scientist in the Perceiving Systems department of Dr. Michael Black at the MPI-IS. 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, 3D motion estimation and visual scene understanding with a particular focus on integrating rich prior knowledge and deep learning for improving perception in intelligent 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 on stereo reconstruction and optical flow estimation is ranked amongst the top-performing methods in several international competitions. 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 DFG, the German Pattern Recognition Award, the Ernst-Schoemperlen Award and the KIT Doctoral Award. In 2013, he received the CVPR best paper runner up award for his work on probabilistic visual self-localization. He also received the best paper award at GCPR 2015 and 3DV 2015 as well as the best student paper award at 3DV 2017. He is an associate member of the Max Planck ETH Center for Learning Systems and the International Max Planck Research School for Intelligent Systems, and serves as an area chair and associate editor for several computer vision conferences and journals (CVPR, ICCV, ECCV, PAMI, IJCV).