AlignPose: Generalizable 6D Pose Estimation via Multi-view Feature-metric Alignment
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910244169318400 |
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| author | Mikeštíková, Anna Šárová Fourmy, Médéric Cífka, Martin Sivic, Josef Petrik, Vladimir |
| author_facet | Mikeštíková, Anna Šárová Fourmy, Médéric Cífka, Martin Sivic, Josef Petrik, Vladimir |
| contents | Single-view RGB model-based object pose estimation methods achieve strong generalization but are fundamentally limited by depth ambiguity, clutter, and occlusions. Multi-view pose estimation methods have the potential to solve these issues, but existing works rely on precise single-view pose estimates or lack generalization to unseen objects. We address these challenges via the following three contributions. First, we introduce AlignPose, a 6D object pose estimation method that aggregates information from multiple extrinsically calibrated RGB views and does not require any object-specific training or symmetry annotation. Second, the key component of this approach is a new multi-view feature-metric refinement specifically designed for object pose. It optimizes a single, consistent world-frame object pose by minimizing the feature discrepancy between on-the-fly rendered object features and observed image features across all views simultaneously. Third, we report extensive experiments on six datasets (YCB-V, T-LESS, HouseCat6D, ITODD-MV, IPD, XYZ-IBD) using the BOP benchmark evaluation and show that AlignPose outperforms other published methods, especially on challenging industrial datasets where multiple views are readily available in practice. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_20538 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AlignPose: Generalizable 6D Pose Estimation via Multi-view Feature-metric Alignment Mikeštíková, Anna Šárová Fourmy, Médéric Cífka, Martin Sivic, Josef Petrik, Vladimir Computer Vision and Pattern Recognition Single-view RGB model-based object pose estimation methods achieve strong generalization but are fundamentally limited by depth ambiguity, clutter, and occlusions. Multi-view pose estimation methods have the potential to solve these issues, but existing works rely on precise single-view pose estimates or lack generalization to unseen objects. We address these challenges via the following three contributions. First, we introduce AlignPose, a 6D object pose estimation method that aggregates information from multiple extrinsically calibrated RGB views and does not require any object-specific training or symmetry annotation. Second, the key component of this approach is a new multi-view feature-metric refinement specifically designed for object pose. It optimizes a single, consistent world-frame object pose by minimizing the feature discrepancy between on-the-fly rendered object features and observed image features across all views simultaneously. Third, we report extensive experiments on six datasets (YCB-V, T-LESS, HouseCat6D, ITODD-MV, IPD, XYZ-IBD) using the BOP benchmark evaluation and show that AlignPose outperforms other published methods, especially on challenging industrial datasets where multiple views are readily available in practice. |
| title | AlignPose: Generalizable 6D Pose Estimation via Multi-view Feature-metric Alignment |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.20538 |