AlignPose: Generalizable 6D Pose Estimation via Multi-view Feature-metric Alignment

• URL: http://arxiv.org/abs/2512.20538v1
• Date: Tue, 23 Dec 2025 17:29:08 GMT
• Title: AlignPose: Generalizable 6D Pose Estimation via Multi-view Feature-metric Alignment
• Authors: Anna Šárová Mikeštíková, Médéric Fourmy, Martin Cífka, Josef Sivic, Vladimir Petrik,
• Abstract summary: We introduce AlignPose, a 6D object pose estimation method that aggregates information from multiple RGB views.<n>Key component of this approach is a new multi-view feature-metric refinement specifically designed for object pose.<n>It optimize a single, consistent world-frame object pose minimizing the feature discrepancy between on-the-fly rendered object features and observed image features.
• Score: 18.198789096671245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 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 four datasets (YCB-V, T-LESS, ITODD-MV, HouseCat6D) 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.

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