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

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

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

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

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 ...
IEEE

Rethinking Masked Representation Learning for 3D Point Cloud Understanding

Abstract: Self-supervised point cloud representation learning aims to acquire robust and general feature representations from unlabeled data. Recently, masked point modeling-based methods have shown ...
IEEE

Applying Machine Learning on Big Data With Apache Spark

Abstract: The exponential growth of data in the digital age has necessitated the development of frameworks capable of efficiently handling and processing vast datasets. This paper explores the ...
IEEE

Student-Led Tutorials Interactions and Learning in Electromagnetism

Abstract: Contribution: This study identifies the types of interaction that contribute to student learning with student-led tutorials (SLTs). The quality of these interactions include peer discussion, ...