For intelligent transportation systems, moving object segmentation (MOS) provides valuable
information for robots and intelligent vehicles, such as collision avoidance, path planning …

The ability to detect and segment moving objects in a scene is essential for building
consistent maps, making future state predictions, avoiding collisions, and planning. In this …

Scan data of urban environments often include representations of dynamic objects, such as
vehicles, pedestrians, and so forth. However, when it comes to constructing a 3D point cloud …

Multi-beam LiDAR sensors, as used on autonomous vehicles and mobile robots, acquire
sequences of 3D range scans (“frames”). Each frame covers the scene sparsely, due to …

Understanding the scene is key for autonomously navigating vehicles, and the ability to
segment the surroundings online into moving and non-moving objects is a central ingredient …

A key challenge for autonomous vehicles is to navigate in unseen dynamic environments.
Separating moving objects from static ones is essential for navigation, pose estimation, and …

Semantic scene completion (SSC) aims to jointly estimate the complete geometry and
semantics of a scene, assuming partial sparse input. In the last years following the …

Accurate moving object segmentation is an es-sential task for autonomous driving. It can
provide effective information for many downstream tasks, such as collision avoidance, path …

Mobile robots that navigate in unknown environments need to be constantly aware of the
dynamic objects in their surroundings for mapping, localization, and planning. It is key to …

Simultaneous Localization and Mapping (SLAM) is considered to be an essential capability
for intelligent vehicles and mobile robots. However, most of the current lidar SLAM …