Train in Germany, Test in The USA: Making 3D Object Detectors Generalize

Understand

In the domain of autonomous driving, deep learning has substantially improved the 3D object detection accuracy for LiDAR and stereo camera data alike.

• While deep networks are great at generalization, they are also notorious to over-fit to all kinds of spurious artifacts, such as brightness, car sizes and models, that may appear consistently throughout the data.
• In fact, most datasets for autonomous driving are collected within a narrow subset of cities within one country, typically under similar weather conditions.
• In this paper we consider the task of adapting 3D object detectors from one dataset to another.