Scene understanding plays a crucial role in autonomous driving by utilizing sensory data for
contextual information extraction and decision making. Beyond modeling advances, the …

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 3D LiDAR semantic segmentation is fundamental for autonomous driving. Several
Unsupervised Domain Adaptation (UDA) methods for point cloud data have been recently …

A holistic semantic scene understanding exploiting all available sensor modalities is a core
capability to master self-driving in complex everyday traffic. To this end, we present the …

State-of-the-art methods for driving-scene LiDAR-based perception (including point cloud
semantic segmentation, panoptic segmentation and 3D detection, etc.) often project the …

Predicting how the world can evolve in the future is crucial for motion planning in
autonomous systems. Classical methods are limited because they rely on costly human …

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 …

3D semantic segmentation is a fundamental task for robotic and autonomous driving
applications. Recent works have been focused on using deep learning techniques, whereas …

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 …

Semantic perception is a core building block in autonomous driving, since it provides
information about the drivable space and location of other traffic participants. For learning …