Torsten Sattler
Torsten Sattler is an associate professor in the research group Computer vision and medical image analysis. His main research interests focus around developing robust and reliable 3D computer vision algorithms for applications such as Mixed Reality, Self-Driving Cars, and Robotics. To this end, Torsten works on integrating higher-level scene understanding into techniques such as visual localization and mapping. He is further interested in real-time computer vision algorithms and machine learning for computer vision tasks.
Showing 12 publications
Understanding the Limitations of CNN-based Absolute Camera Pose Regression
Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization
Project AutoVision: Localization and 3D Scene Perception for an Autonomous Vehicle with a Multi-Camera System
Efficient 2D-3D Matching for Multi-Camera Visual Localization
SurfelMeshing: Online Surfel-Based Mesh Reconstruction
A cross-season correspondence dataset for robust semantic segmentation
Night-to-day image translation for retrieval-based localization
Hybrid scene Compression for Visual Localization
Bad slam: Bundle adjusted direct RGB-D slam
D2-Net: A Trainable CNN for Joint Description and Detection of Local Features
Real-Time Dense Mapping for Self-Driving Vehicles using Fisheye Cameras
Incremental visual-inertial 3d mesh generation with structural regularities
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Showing 2 research projects
Understanding and Overcoming the Limitations of Convolutional Neural Networks for Visual Localization (VisLocLearn)
Vision and machine learning for collaborative robotics