Advancing Precision in Multi-Point Cloud Fusion Environments

2025/08/05 by Alibekov, Ulugbek, Staderini, Vanessa, Schneider, Philipp +1 · 1 citation
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Graphics (cs.GR)

paper · doi:10.48550/arxiv.2508.03179

Abstract

This research focuses on visual industrial inspection by evaluating point clouds and multi-point cloud matching methods. We also introduce a synthetic dataset for quantitative evaluation of registration method and various distance metrics for point cloud comparison. Additionally, we present a novel CloudCompare plugin for merging multiple point clouds and visualizing surface defects, enhancing the accuracy and efficiency of automated inspection systems.

Citations

• Cross-source Point Cloud Registration: Challenges, Progress and Prospects
• Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion
• PCRNet: Point Cloud Registration Network using PointNet Encoding
• PCN: Point Completion Network

Cited by

• 3D-RE-GEN: 3D Reconstruction of Indoor Scenes with a Generative Framework

Related

• Target-less registration of point clouds: A review
• Incremental Multiview Point Cloud Registration
• A comprehensive survey on point cloud registration
• 3D Point Cloud Registration with Learning-based Matching Algorithm
• Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences