Advancing Precision in Multi-Point Cloud Fusion Environments
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911093040873472 |
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| author | Alibekov, Ulugbek Staderini, Vanessa Schneider, Philipp Antensteiner, Doris |
| author_facet | Alibekov, Ulugbek Staderini, Vanessa Schneider, Philipp Antensteiner, Doris |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03179 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advancing Precision in Multi-Point Cloud Fusion Environments Alibekov, Ulugbek Staderini, Vanessa Schneider, Philipp Antensteiner, Doris Computer Vision and Pattern Recognition Graphics 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. |
| title | Advancing Precision in Multi-Point Cloud Fusion Environments |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2508.03179 |