ViewPCL: a point cloud based active learning method for multi-view segmentation
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912432137437184 |
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| author | Hilaire, Christian Didari, Sima |
| author_facet | Hilaire, Christian Didari, Sima |
| contents | We propose a novel active learning framework for multi-view semantic segmentation. This framework relies on a new score that measures the discrepancy between point cloud distributions generated from the extra geometrical information derived from the model's prediction across different views. Our approach results in a data efficient and explainable active learning method. The source code is available at https://github.com/chilai235/viewpclAL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13043 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ViewPCL: a point cloud based active learning method for multi-view segmentation Hilaire, Christian Didari, Sima Computer Vision and Pattern Recognition We propose a novel active learning framework for multi-view semantic segmentation. This framework relies on a new score that measures the discrepancy between point cloud distributions generated from the extra geometrical information derived from the model's prediction across different views. Our approach results in a data efficient and explainable active learning method. The source code is available at https://github.com/chilai235/viewpclAL. |
| title | ViewPCL: a point cloud based active learning method for multi-view segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.13043 |