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

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 …

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 …

LiDAR-based scene flow estimation (SFE) and moving object segmentation (MOS) are
important tasks with broad-ranging applications in autonomous driving, such as traffic …

Accurate and robust localization is one of the key modules of autonomous driving and multi-
sensor robotic systems in complex urban scenes. Compared with camera-based …

Detecting traffic participants is an essential and age-old problem in autonomous driving.
Recently, the recognition of moving objects has emerged as a major issue in this field for …

The perception of moving objects is crucial for autonomous robots performing collision
avoidance in dynamic environments. LiDARs and cameras tremendously enhance scene …

The key to autonomous navigation in unmanned systems is the ability to recognize static
and moving objects in the environment and to support the task of predicting the future state …

Identifying moving objects is a crucial capability for autonomous navigation, consistent map
generation, and future trajectory prediction of objects. In this paper, we propose a novel …

In this work, we address the problem of unsupervised moving object segmentation (MOS) in
4D LiDAR data recorded from a stationary sensor, where no ground truth annotations are …