The perception of the surrounding environment is a key requirement for autonomous driving
systems, yet the computation of an accurate semantic representation of the scene starting …

In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to
improve the perception performance of autonomous vehicles. We present a robust …

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

With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …

LiDAR and camera are two modalities available for 3D semantic segmentation in
autonomous driving. The popular LiDAR-only methods severely suffer from inferior …

Abstract 3D LiDAR (light detection and ranging) semantic segmentation is important in
scene understanding for many applications, such as auto-driving and robotics. For example …

Multi-agent collaborative perception as a potential application for vehicle-to-everything
communication could significantly improve the perception performance of autonomous …

In recent years, three-dimensional (3D) machine vision techniques have been widely
employed in agriculture and food systems, leveraging advanced deep learning …

Test-time adaptation approaches have recently emerged as a practical solution for handling
domain shift without access to the source domain data. In this paper, we propose and …

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 …