Eff-GRot: Efficient and Generalizable Rotation Estimation with Transformers
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918257867358208 |
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| author | Mathioulakis, Fanis Radevski, Gorjan Tuytelaars, Tinne |
| author_facet | Mathioulakis, Fanis Radevski, Gorjan Tuytelaars, Tinne |
| contents | We introduce Eff-GRot, an approach for efficient and generalizable rotation estimation from RGB images. Given a query image and a set of reference images with known orientations, our method directly predicts the object's rotation in a single forward pass, without requiring object- or category-specific training. At the core of our framework is a transformer that performs a comparison in the latent space, jointly processing rotation-aware representations from multiple references alongside a query. This design enables a favorable balance between accuracy and computational efficiency while remaining simple, scalable, and fully end-to-end. Experimental results show that Eff-GRot offers a promising direction toward more efficient rotation estimation, particularly in latency-sensitive applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18784 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Eff-GRot: Efficient and Generalizable Rotation Estimation with Transformers Mathioulakis, Fanis Radevski, Gorjan Tuytelaars, Tinne Computer Vision and Pattern Recognition Machine Learning We introduce Eff-GRot, an approach for efficient and generalizable rotation estimation from RGB images. Given a query image and a set of reference images with known orientations, our method directly predicts the object's rotation in a single forward pass, without requiring object- or category-specific training. At the core of our framework is a transformer that performs a comparison in the latent space, jointly processing rotation-aware representations from multiple references alongside a query. This design enables a favorable balance between accuracy and computational efficiency while remaining simple, scalable, and fully end-to-end. Experimental results show that Eff-GRot offers a promising direction toward more efficient rotation estimation, particularly in latency-sensitive applications. |
| title | Eff-GRot: Efficient and Generalizable Rotation Estimation with Transformers |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2512.18784 |