Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

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 …

The robustness of 3D perception systems under natural corruptions from environments and
sensors is pivotal for safety-critical applications. Existing large-scale 3D perception datasets …

Abstract 3D object detection is an important task in autonomous driving to perceive the
surroundings. Despite the excellent performance, the existing 3D detectors lack the …

Abstract 3D object detection is a central task for applications such as autonomous driving, in
which the system needs to localize and classify surrounding traffic agents, even in the …

Most object detection methods for autonomous driving usually assume a onsistent feature
distribution between training and testing data, which is not always the case when weathers …

Abstract We present Neural Fields for LiDAR (NFL), a method to optimise a neural field
scene representation from LiDAR measurements, with the goal of synthesizing realistic …

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) …

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

In the past ten years, the use of 3D Time-of-Flight (ToF) LiDARs in mobile robotics has
grown rapidly. Based on our accumulation of relevant research, this article systematically …