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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2303.02698 |
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| _version_ | 1866909339262910464 |
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| author | Kolpakov, Alexander Werman, Michael |
| author_facet | Kolpakov, Alexander Werman, Michael |
| contents | Robust Affine Matching with Grassmannians (RoAM) is a new algorithm to perform affine registration of point clouds. The algorithm is based on minimizing the Frobenius distance between two elements of the Grassmannian. For this purpose, an indefinite relaxation of the Quadratic Assignment Problem (QAP) is used, and several approaches to affine feature matching are studied and compared. Experiments demonstrate that RoAM is more robust to noise and point discrepancy than previous methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_02698 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Robust affine point matching via quadratic assignment on Grassmannians Kolpakov, Alexander Werman, Michael Computer Vision and Pattern Recognition Robust Affine Matching with Grassmannians (RoAM) is a new algorithm to perform affine registration of point clouds. The algorithm is based on minimizing the Frobenius distance between two elements of the Grassmannian. For this purpose, an indefinite relaxation of the Quadratic Assignment Problem (QAP) is used, and several approaches to affine feature matching are studied and compared. Experiments demonstrate that RoAM is more robust to noise and point discrepancy than previous methods. |
| title | Robust affine point matching via quadratic assignment on Grassmannians |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2303.02698 |