There is active research targeting local image manipulations that can fool deep neural
networks (DNNs) into producing incorrect results. This paper examines a type of global …

Image synthesis designed for machine learning applications provides the means to
efficiently generate large quantities of training data while controlling the generation process …

Sensor models are essential for virtual testing of Advanced Driver Assistance
Systems/Autonomous Driving (ADAS/AD) functions. This article gives an overview of the …

One of the fundamental challenges in the design of perception systems for autonomous
vehicles is validating the performance of each algorithm under a comprehensive variety of …

Autonomous robot manipulation involves estimating the translation and orientation of the
object to be manipulated as a 6-degree-of-freedom (6D) pose. Methods using RGB-D data …

RGB cameras are arguably one of the most relevant sensors for autonomous driving
applications. It is undeniable that failures of vehicle cameras may compromise the …

Contemporary approaches frame the color constancy problem as learning camera specific
illuminant mappings. While high accuracy can be achieved on camera specific data, these …

RGB cameras are one of the most relevant sensors for autonomous driving applications. It is
undeniable that failures of vehicle cameras may compromise the autonomous driving task …

Robustness is a crucial factor for the successful deployment of robots in unstructured
environments, particularly in the domain of Simultaneous Localization and Mapping (SLAM) …

Training data is the key ingredient for deep learning approaches, but difficult to obtain for the
specialized domains often encountered in robotics. We describe a synthesis pipeline …