IEEE

NEUF: Learning Point Cloud Quality by Non-Euclidean Fast Filtering

Abstract: This paper addresses the problem of no reference visual quality assessment in point clouds, useful for extended reality communication service such as remote surgery and education. Accurate, ...
IEEE

Hierarchical Distortion Learning for Fast Lossy Compression of Point Clouds

Abstract: The growth of 3D point cloud applications requires efficient compression techniques for high-quality and low-latency services. Recently, learning-based point cloud compression models have ...
IEEE

Learning-Based Reflection-Aware Virtual Point Removal for Large-Scale 3D Point Clouds

Abstract: 3D point clouds are widely used for robot perception and navigation. LiDAR sensors can provide large scale 3D point clouds (LS3DPC) with a certain level of accuracy in common environment.
IEEE

Efficient Sampling and Grouping Acceleration for Point Cloud Deep Learning via Single Coordinate Comparison

Abstract: With the focus on three-dimensional (3D) applications, the importance of applying deep learning to point clouds have been growing recently. It is known that mapping operations including ...
GitHub

Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud Segmentation

3D point clouds are discrete samples of continuous surfaces which can be used for various applications. However, the lack of true connectivity information, i.e., edge information, makes point cloud ...
IEEE

Transitioning From Federated Learning to Quantum Federated Learning in Internet of Things: A Comprehensive Survey

Abstract: Quantum Federated Learning (QFL) recently becomes a promising approach with the potential to revolutionize Machine Learning (ML). It merges the established strengths of classical Federated ...
IEEE

PointWavelet: Learning in Spectral Domain for 3-D Point Cloud Analysis

Abstract: With recent success of deep learning in 2-D visual recognition, deep-learning-based 3-D point cloud analysis has received increasing attention from the community, especially due to the rapid ...
IEEE

SphereNet: Learning a Noise-Robust and General Descriptor for Point Cloud Registration

Abstract: Point cloud registration aims to estimate a transformation that aligns point clouds collected from different perspectives. In learning-based point cloud registration, a robust descriptor is ...
IEEE

A Sequential and Asynchronous Federated Learning Framework for Railway Point Machine Fault Diagnosis With Imperfect Data Transmission

Abstract: Fault diagnosis of railway assets has drawn the interest of both the scholarly and engineering communities. Federated learning (FL) enables training models across distributed assets to ...
IEEE

Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey

Abstract: Future Internet involves several emerging technologies such as 5G and beyond 5G networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of Things (IoTs). Moreover, ...
IEEE

Configurable Multi-Precision Floating-Point Multiplier Architecture Design for Computation in Deep Learning

Abstract: The increasing AI applications demands efficient computing capabilities to support a huge amount of calculations. Among the related arithmetic operations, multiplication is an indispensable ...
IEEE

LDF-I2P: Learning Discriminative Cross-Modality Features for Image-to-Point Cloud Registration

Abstract: Image-to-point-cloud (I2P) registration has garnered substantial research attention in domains such as robotic navigation, localization, and manipulation, achieving notable advancements in ...