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

Toward Visual Interaction: Hand Segmentation by Combining 3-D Graph Deep Learning and Laser Point Cloud for Intelligent Rehabilitation

Abstract: Against the backdrop of the increasing trend of aging population in China and even globally, the demand for hand function rehabilitation is growing day by day, and human-machine interaction ...
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

Hard-Soft Pseudo Labels Guided Semi-Supervised Learning for Point Cloud Classification

Abstract: Point clouds are widely applied in 3D visual sensing and perception. However, manually annotating point clouds is much more tedious and time-consuming than that for 2D images. Fortunately, ...
IEEE

AUV Path Planning and Object Tracking Algorithm Based on Reinforcement Learning Under Point Cloud Observation

Abstract: Autonomous underwater vehicles (AUV) play an important role in the process of human exploration of the ocean. However, the existing AUV control methods are faced with the problem of ...
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 ...
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

PointOBB: Learning Oriented Object Detection via Single Point Supervision

Abstract: Single point-supervised object detection is gaining attention due to its cost-effectiveness. However, existing approaches focus on generating horizontal bounding boxes (HBBs) while ignoring ...
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 Tutorial on Quantum Computing and Deep Learning with Quantum Deep Neural Networks

Abstract: Quantum Computing (QC) technology and Deep Learning (DL) science have garnered significant attention for their potential to revolutionize computation. This paper introduces the basic ...