IEEE

Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning

Abstract: Weakly supervised point cloud semantic segmentation methods that require 1% or fewer labels with the aim of realizing almost the same performance as fully supervised approaches have recently ...
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

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

Detecting Java Compiled Malware using Machine Learning Techniques

Abstract: Malicious software using Java Language in order to implement the attack evolved rapidly in the past years. Initially we were used to find malicious Applets and exploitation methods to escape ...
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, ...