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Semantic Visual Localization
(2018)2018 IEEE/CVF Conference on Computer Vision and Pattern RecognitionRobust visual localization under a wide range of viewing conditions is a fundamental problem in computer vision. Handling the difficult cases of this problem is not only very challenging but also of high practical relevance, e.g., in the context of life-long localization for augmented reality or autonomous robots. In this paper, we propose a novel approach based on a joint 3D geometric and semantic understanding of the world, enabling it ...Conference Paper -
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
(2018)2018 IEEE/CVF Conference on Computer Vision and Pattern RecognitionWe seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are three-fold. First, we develop a new large-scale visual localization method targeted for indoor environments. The method proceeds along three steps: (i) efficient retrieval of candidate poses that ensures scalability to large-scale environments, (ii) pose estimation using dense matching ...Conference Paper -
VSO: Visual Semantic Odometry
(2018)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2018Conference Paper -
Hybrid camera pose estimation
(2018)2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)Conference Paper -
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
(2018)2018 IEEE/CVF Conference on Computer Vision and Pattern RecognitionConference Paper -
Semantic Match Consistency for Long-Term Visual Localization
(2018)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2018Conference Paper -
Towards Robust Visual Odometry with a Multi-Camera System
(2018)2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)We present a visual odometry (VO) algorithm for a multi-camera system and robust operation in challenging environments. Our algorithm consists of a pose tracker and a local mapper. The tracker estimates the current pose by minimizing photometric errors between the most recent keyframe and the current frame. The mapper initializes the depths of all sampled feature points using plane-sweeping stereo. To reduce pose drift, a sliding window ...Conference Paper