IEEE

Enhanced Local and Global Learning for Rotation-Invariant Point Cloud Representation

Abstract: Various studies have been undertaken to learn point cloud representations that are both discriminative and robust. However, most of them suffer from rotation disturbance and insufficient ...
IEEE

Domain Adaptation on Point Clouds via Multi-Modal Representation Learning

Abstract: Enhancing cross-domain performance of 3D point cloud neural networks remains a formidable task due to subtle variations in feature distributions among datasets, limiting their ...
IEEE

Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis

Abstract: Discrete point cloud objects lack sufficient shape descriptors of 3D geometries. In this paper, we present a novel method for aggregating hypothetical curves in point clouds. Sequences of ...
IEEE

Learning Point Cloud Completion without Complete Point Clouds: A Pose-Aware Approach

Abstract: Point cloud completion is to restore complete 3D scenes and objects from incomplete observations or limited sensor data. Existing fully-supervised methods rely on paired datasets of ...
IEEE

A Comprehensive Deep Learning-Based Outlier Removal Method for Multibeam Bathymetric Point Cloud

Abstract: To address the drawbacks that current multibeam bathymetric outlier removal methods lack repeatability, often require parameter adjustment for different regions, still require a lot of ...
IEEE

Deep Learning Based Point Cloud Processing Techniques

Abstract: In this study, deep learning techniques and algorithms used in point cloud processing have been analysed. Methods, technical properties and algorithms developed for 3D Object Classification ...
IEEE

Point Cloud Denoising using Deep Learning

Abstract: The emergence of uncontrollable noise from diverse sources possess several difficulties in scanning 3D objects. In the case of animals in the wild this is especially hard to manage since ...
IEEE

Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering

Abstract: Point cloud filtering and normal estimation are two fundamental research problems in the 3D field. Existing methods usually perform normal estimation and filtering separately and often show ...
IEEE

Weakly Supervised Learning for Point Cloud Semantic Segmentation With Dual Teacher

Abstract: Point cloud semantic segmentation has achieved considerable progress in the past decade. To alleviate expensive data annotation efforts, weakly supervised learning methods are preferable, ...
IEEE

2955-2025 - IEEE Recommended Practice for Creating Self-Learning Tutorials and Side-by-Side Learning

Abstract: A methodology for creating self-learning tutorials, including tutorials that use screencast technology, is described in this recommended practice. Methods and practices that are applicable ...
IEEE

Exploring an Innovative Deep Learning Solution for Acupuncture Point Localization on the Weak Feature Body Surface of the Human Back

Abstract: In current clinical practice, the localization of human acupuncture points relies extensively on the subjective experience of physicians. Therefore, despite being a crucial basic content of ...