The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of
deep learning (DL). However, the latter faces various issues, including the lack of data or …

With the growing availability of extensive 3D datasets and the rapid progress in
computational power, deep learning (DL) has emerged as a highly promising approach for …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

The utilization of 3D point clouds acquired via Light Detection and Ranging (LiDAR) is
widespread in the fields of autonomous driving, satellite remote sensing, and spatial …

Multirobot system for manufacturing is an Industry Internet of Things (IIoT) paradigm with
significant operational cost savings and productivity improvement, where unmanned aerial …

In autonomous driving, even a meticulously trained model can encounter failures when
facing unfamiliar scenarios. One of these scenarios can be formulated as an online …

Domain adaptive LiDAR point cloud segmentation aims to learn a target segmentation
model from labeled source point clouds and unlabelled target point clouds, which has …

Abstract 3D Lidar has been widely used in auto drive systems owing to its high resolution,
strong imaging ability, and long detection distance. The point cloud data (PCD) is generated …

With the widespread deployment of 3D sensors, point cloud analysis has become an
important topic in the field of industrial information. This article proposes a novel zero-shot …

Deep neural networks have been shown to produce incorrect predictions when
imperceptible perturbations are introduced into the clean input. This phenomenon has …