Tensor-Based Synchronization and the Low-Rankness of the Block Trifocal Tensor
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915001388761088 |
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| author | Miao, Daniel Lerman, Gilad Kileel, Joe |
| author_facet | Miao, Daniel Lerman, Gilad Kileel, Joe |
| contents | The block tensor of trifocal tensors provides crucial geometric information on the three-view geometry of a scene. The underlying synchronization problem seeks to recover camera poses (locations and orientations up to a global transformation) from the block trifocal tensor. We establish an explicit Tucker factorization of this tensor, revealing a low multilinear rank of $(6,4,4)$ independent of the number of cameras under appropriate scaling conditions. We prove that this rank constraint provides sufficient information for camera recovery in the noiseless case. The constraint motivates a synchronization algorithm based on the higher-order singular value decomposition of the block trifocal tensor. Experimental comparisons with state-of-the-art global synchronization methods on real datasets demonstrate the potential of this algorithm for significantly improving location estimation accuracy. Overall this work suggests that higher-order interactions in synchronization problems can be exploited to improve performance, beyond the usual pairwise-based approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09313 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tensor-Based Synchronization and the Low-Rankness of the Block Trifocal Tensor Miao, Daniel Lerman, Gilad Kileel, Joe Computer Vision and Pattern Recognition Numerical Analysis Optimization and Control The block tensor of trifocal tensors provides crucial geometric information on the three-view geometry of a scene. The underlying synchronization problem seeks to recover camera poses (locations and orientations up to a global transformation) from the block trifocal tensor. We establish an explicit Tucker factorization of this tensor, revealing a low multilinear rank of $(6,4,4)$ independent of the number of cameras under appropriate scaling conditions. We prove that this rank constraint provides sufficient information for camera recovery in the noiseless case. The constraint motivates a synchronization algorithm based on the higher-order singular value decomposition of the block trifocal tensor. Experimental comparisons with state-of-the-art global synchronization methods on real datasets demonstrate the potential of this algorithm for significantly improving location estimation accuracy. Overall this work suggests that higher-order interactions in synchronization problems can be exploited to improve performance, beyond the usual pairwise-based approaches. |
| title | Tensor-Based Synchronization and the Low-Rankness of the Block Trifocal Tensor |
| topic | Computer Vision and Pattern Recognition Numerical Analysis Optimization and Control |
| url | https://arxiv.org/abs/2409.09313 |