Abstract: LiDAR point cloud data is essential in autonomous driving, robotic navigation, and 3D modeling. However, noise caused by sensor errors and environmental factors degrades data quality and ...

Abstract: The demand for 3D scanning of workpiece geometries in automated assembly within workshops is increasingly critical, playing a vital role in the process. Point cloud registration, as an ...

In this work, we propose a novel, fast, and memory-efficient unsupervised statistical method, combined with an unsupervised deep learning (DL) model, for de-snowing 3D LiDAR point clouds in a fully ...

Abstract: Semantic segmentation of point clouds is an essential task for understanding the environment in autonomous driving and robotics. Recent range-based works achieve real-time efficiency, while ...

Abstract: In the context of Vehicle-to-Everything (V2X) communications, optimizing data transmission is crucial. This study uses evolutionary game theory to build a profit model for channel data ...

Abstract: With the increasing demand of high-precision data acquisition card in application, noise suppression in the process of signal acquisition becomes very important. The existence of noise will ...

Abstract: To address the issue of low accuracy in 3D point cloud registration, we present a novel iterative closest point (ICP) algorithm for point registration using the stochastic differential ...

Abstract: The extensive adoption of cloud computing platforms in storing and processing data have brought forth a new age of efficiency in the way data is stored, processed and managed, requiring new ...

Introduction: Three-dimensional (3D) point clouds acquired by LiDAR are fundamental for applications such as autonomous navigation, mobile robotics, infrastructure inspection, and cultural-heritage ...

Abstract: This innovative practice full paper describes how to integrate generative Artificial Intelligence (AI) with Data Structures and Algorithm Analysis (CS2) homework at Oklahoma State University ...