Publications
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Cloudlet-based large-scale 3D reconstruction using real-time data from mobile depth cameras Ghent University
Automatic camera to laser calibration for high accuracy mobile mapping systems using INS Ghent University
Automatic detection of a one dimensional ranging pole for robust external camera calibration in mobile mapping Ghent University
Accuracy enhancement of the measuring probe for a camera based mobile CMM KU Leuven
Mobile CMMs (Coordinate Measuring Machines) are often the only solution for measuring large objects. Some of those mobile CMMs use probes provided with LEDs in order to identify the probe position. This paper proposes a new probe LED configuration that improves the accuracy of the measuring probe for a camera based mobile CMM by 50 percent. The redesign is obtained by taking into account the direction dependent uncertainty zones of the LED ...
Mobile camera localization using Apollonius circles and virtual landmarks University of Antwerp
We investigate the localization of a camera subject to a planar motion with horizontal optical axis in the presence of known vertical landmarks. Under these assumptions, a calibrated camera can measure the distance to the viewed landmarks. We propose to replace the trilateration method by intersecting a pair of Chasles-Apollonius circles. In the case of square pixels but unknown focal length we introduce a new method to recover the camera ...
End-to-end learning of driving models with surround-view cameras and route planners KU Leuven
© Springer Nature Switzerland AG 2018. For human drivers, having rear and side-view mirrors is vital for safe driving. They deliver a more complete view of what is happening around the car. Human drivers also heavily exploit their mental map for navigation. Nonetheless, several methods have been published that learn driving models with only a front-facing camera and without a route planner. This lack of information renders the self-driving task ...
WESPE: Weakly Supervised Photo Enhancer for Digital Cameras KU Leuven
© 2018 IEEE. Low-end and compact mobile cameras demonstrate limited photo quality mainly due to space, hardware and budget constraints. In this work, we propose a deep learning solution that translates photos taken by cameras with limited capabilities into DSLR-quality photos automatically. We tackle this problem by introducing a weakly supervised photo enhancer (WESPE)-a novel image-to-image Generative Adversarial Network-based architecture. ...