提升多点云融合精度，助力工业视觉检测
Advancing Precision in Multi-Point Cloud Fusion Environments
- 构建合成数据集评估点云配准与距离度量方法
- 开发CloudCompare插件实现多点云融合与缺陷可视化
- 适用于工业质检中的自动化高精度检测场景
本研究聚焦于基于点云的工业视觉检测，评估点云及多点云匹配方法。提出一个用于定量评估配准方法的合成数据集，以及多种点云比较的距离度量方法。此外，开发了一款新的CloudCompare插件，支持多点云融合与表面缺陷可视化，显著提升了自动化检测系统的精度与效率。
原文摘要 · Abstract (English)
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.
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