{"ID":2826577,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.18784","arxiv_id":"2512.18784","title":"Eff-GRot: Efficient and Generalizable Rotation Estimation with Transformers","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2512.18784","url_pdf":"https://arxiv.org/pdf/2512.18784v1","authors":"[\"Fanis Mathioulakis\",\"Gorjan Radevski\",\"Tinne Tuytelaars\"]","published":"2025-12-21T15:57:13Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}