Abstract: Securing data transactions across computer networks presents significant challenges, particularly concerning data privacy and cybersecurity threats. These issues are particularly critical in ...
Abstract: LiDAR and photogrammetry are active and passive remote sensing techniques for point cloud acquisition, respectively, offering complementary advantages and heterogeneous. Due to the ...
Abstract: Raw face point clouds obtained from scanning are often incomplete, resulting in a loss of structural details and posing challenges for many tasks, such as facial surgery navigation, face ...
Abstract: This paper introduces the Elemental Composite Prototypical Network (ECPN), a novel approach to few-shot learning (FSL) in outdoor 3D point cloud object detection. Such point clouds are ...
Abstract: This paper introduces an approach for Activity Recognition by integrating channel-wise attention with Motion of Oriented Gradients (MOG) and Appearance Information (AI) alongside a ...
LiDAR point cloud semantic segmentation enables the robots to obtain fine-grained semantic information of the surrounding environment. Recently, many works project the point cloud onto the 2D image ...
Abstract: Point cloud registration, which estimates a rigid transformation matrix between two point clouds, is a fundamental process in numerous applications. While existing detector-free techniques ...
Abstract: Point cloud completion concerns the inference of the completed geometries for real-scanned point clouds that are sparse and incomplete due to occlusion, noise, and viewpoint. Previous ...
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Abstract: 3D object detection from LiDAR data for autonomous driving has been making remarkable strides in recent years. Among the state-of-the-art methodologies, encoding point clouds into a bird's ...
Abstract: Deep unfolding networks (DUNs), renowned for their in-terpretability and superior performance, have invigorated the realm of compressive sensing (CS). Nonetheless, existing DUNs frequently ...
Abstract: Edge Learning environments, characterized by limited wireless resources, encounter significant bottlenecks in network performance, particularly in Federated Learning (FL) tasks. Current ...