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Rainy screens: Collecting rainy datasets, indoors
Acquisition of data with adverse conditions in robotics is a cumbersome task due to the difficulty in guaranteeing proper ground truth and synchronising with desired weather conditions.
Don't Worry About the Weather: Unsupervised Condition-Dependent Domain Adaptation
Modern models that perform system-critical tasks such as segmentation and localization exhibit good performance and robustness under ideal conditions (i. e. daytime, overcast) but performance degrades quickly and often catastrophically when input conditions change.
Generating All the Roads to Rome: Road Layout Randomization for Improved Road Marking Segmentation
Road markings provide guidance to traffic participants and enforce safe driving behaviour, understanding their semantic meaning is therefore paramount in (automated) driving.
I Can See Clearly Now : Image Restoration via De-Raining
We present a method for improving segmentation tasks on images affected by adherent rain drops and streaks.
The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping
Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view.
