Semantic map construction under bird's-eye view (BEV) plays an essential role in
autonomous driving. In contrast to camera image, LiDAR provides the accurate 3D …

Semantic bird-eye-view (BEV) grid map is a straightforward data representation for semantic
environment perception. It can be conveniently integrated with downstream tasks, such as …

Constructing HD semantic maps is a central component of autonomous driving. However,
traditional pipelines require a vast amount of human efforts and resources in annotating and …

Abstract 3D visual perception tasks, including 3D detection and map segmentation based on
multi-camera images, are essential for autonomous driving systems. In this work, we present …

LiDAR-based semantic perception tasks are critical yet challenging for autonomous driving.
Due to the motion of objects and static/dynamic occlusion, temporal information plays an …

As camera and LiDAR sensors capture complementary information in autonomous driving,
great efforts have been made to conduct semantic segmentation through multi-modality data …

Segmenting or detecting objects in sparse Lidar point clouds are two important tasks in
autonomous driving to allow a vehicle to act safely in its 3D environment. The best …

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited
due to the deployed expensive sensors and time-consuming computation. Camera-based …

Semantic understanding of the surrounding environment is essential for automated vehicles.
The recent publication of the SemanticKITTI dataset stimulates the research on semantic …

Casting semantic segmentation of outdoor LiDAR point clouds as a 2D problem, eg, via
range projection, is an effective and popular approach. These projection-based methods …