Robust and efficient vehicle detection is an important task of environment perception of
intelligent vehicles, which directly affects the behavior decision-making and motion planning …

Scalable systems for automated driving have to reliably cope with an open-world setting.
This means, the perception systems are exposed to drastic domain shifts, like changes in …

Abstract Knowledge transfer from synthetic to real data has been widely studied to mitigate
data annotation constraints in various computer vision tasks such as semantic segmentation …

Robust point cloud parsing under all-weather conditions is crucial to level-5 autonomy in
autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) …

With the success of the 3D deep learning models, various perception technologies for
autonomous driving have been developed in the LiDAR domain. While these models …

LiDAR-based 3D object detection is an indispensable task in advanced autonomous driving
systems. Though impressive detection results have been achieved by superior 3D detectors …

Testing the full autonomy system in simulation is the safest and most scalable way to
evaluate autonomous vehicle performance before deployment. This requires simulating …

LiDAR semantic segmentation (LSS) is a critical task in autonomous driving and has
achieved promising progress. However prior LSS methods are conventionally investigated …

As a popular geometric representation, point clouds have attracted much attention in 3D
vision, leading to many applications in autonomous driving and robotics. One important yet …

Transferring knowledge learned from the labeled source domain to the raw target domain for
unsupervised domain adaptation (UDA) is essential to the scalable deployment of …