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Image-to-image translation for enhanced feature matching, image retrieval and visual localization
(2019)ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information SciencesThe performance of machine learning and deep learning algorithms for image analysis depends significantly on the quantity and quality of the training data. The generation of annotated training data is often costly, time-consuming and laborious. Data augmentation is a powerful option to overcome these drawbacks. Therefore, we augment training data by rendering images with arbitrary poses from 3D models to increase the quantity of training ...Conference Paper -
Efficient 2D-3D Matching for Multi-Camera Visual Localization
(2019)2019 International Conference on Robotics and Automation (ICRA)Conference Paper -
Real-Time Dense Mapping for Self-Driving Vehicles using Fisheye Cameras
(2019)2019 International Conference on Robotics and Automation (ICRA)Conference Paper -
Night-to-Day Image Translation for Retrieval-based Localization
(2019)2019 International Conference on Robotics and Automation (ICRA)Conference Paper -
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
(2019)2019 International Conference on Robotics and Automation (ICRA)Conference Paper -
Project AutoVision: Localization and 3D Scene Perception for an Autonomous Vehicle with a Multi-Camera System
(2019)2019 International Conference on Robotics and Automation (ICRA)Conference Paper -
Hybrid Scene Compression for Visual Localization
(2019)Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)Conference Paper -
Bad slam: Bundle adjusted direct RGB-D slam
(2019)Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)Conference Paper -
Understanding the limitations of cnn-based absolute camera pose regression
(2019)Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)Conference Paper -
D2-Net: A Trainable CNN for Joint Description and Detection of Local Features
(2019)Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)In this work we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions. We propose an approach where a single convolutional neural network plays a dual role: It is simultaneously a dense feature descriptor and a feature detector. By postponing the detection to a later stage, the obtained keypoints are more stable than their traditional counterparts based on early detection of low-level ...Conference Paper