3D semantic segmentation is one of the key tasks for autonomous driving system. Recently,
deep learning models for 3D semantic segmentation task have been widely researched, but …

The autonomous car must recognize the driving environment quickly for safe driving. As the
Light Detection And Range (LiDAR) sensor is widely used in the autonomous car, fast …

Semantic segmentation is an important component in the perception systems of autonomous
vehicles. In this work, we adopt recent advances in both image and point cloud …

In this paper, we introduce SalsaNext for the semantic segmentation of a full 3D LiDAR point
cloud in real-time. SalsaNext is the next version of SalsaNet[1] which has an encoder …

In this paper, we propose PointSeg, a real-time end-to-end semantic segmentation method
for road-objects based on spherical images. We take the spherical image, which is …

Using deep learning, 3D autonomous driving semantic segmentation has become a well-
studied subject, with methods that can reach very high performance. Nonetheless, because …

In this paper, we introduce a deep encoder-decoder network, named SalsaNet, for efficient
semantic segmentation of 3D LiDAR point clouds. SalsaNet segments the road, ie drivable …

3D semantic segmentation is a fundamental task for robotic and autonomous driving
applications. Recent works have been focused on using deep learning techniques, whereas …

Semantic segmentation of LiDAR point clouds is an important task in autonomous driving.
However, training deep models via conventional supervised methods requires large …

Abstract 3D Semantic segmentation is a key element for a variety of applications in robotics
and autonomous vehicles. For such applications, 3D data are usually acquired by LiDAR …