We introduce a simple yet effective fusion method of LiDAR and RGB data to segment
LiDAR point clouds. Utilizing the dense native range representation of a LiDAR sensor and …

Point clouds can be represented in many forms (views), typically, point-based sets, voxel-
based cells or range-based images (ie, panoramic view). The point-based view is …

We propose LU-Net-for LiDAR U-Net, a new method for the semantic segmentation of a 3D
LiDAR point cloud. Instead of applying some global 3D segmentation method such as …

Scene understanding based on LiDAR point cloud is an essential task for autonomous cars
to drive safely, which often employs spherical projection to map 3D point cloud into multi …

LiDAR point-cloud segmentation is an important problem for many applications. For large-
scale point cloud segmentation, the de facto method is to project a 3D point cloud to get a …

Abstract Point-, voxel-, and range-views are three representative forms of point clouds. All of
them have accurate 3D measurements but lack color and texture information. RGB images …

Many point-based semantic segmentation methods have been designed for indoor
scenarios, but they struggle if they are applied to point clouds that are captured by a light …

Semantic segmentation of LiDAR point clouds has been widely studied in recent years, with
most existing methods focusing on tackling this task using a single scan of the environment …

The requirement of fine-grained perception by autonomous driving systems has resulted in
recently increased research in the online semantic segmentation of single-scan LiDAR …

Semantic segmentation of 3D point cloud data is essential for enhanced high-level
perception in autonomous platforms. Furthermore, given the increasing deployment of …