Semantic Prior Guided One-View 6D Pose Estimation for Novel Objects

In many practical 6D object pose estimation scenarios, we often have access to only a single real-world RGB-D reference view per object, typically without CAD models. Existing methods largely rely on explicit 3D models or multi-view data, which limits their scalability. To address this challenging single-reference model-free setting, we propose $\mathbf{\text{OneViewAll}}$, a semantic-prior-guided framework that performs pose estimation via a novel Project-and-Compare paradigm. Instead of relying on computationally expensive CAD-based rendering, our method directly aligns reference and query observations within a projection-equivariant space. OneViewAll progressively integrates hierarchical semantic priors across three levels: (1) $\mathit{\text{category-}}\mathit{\text{and}}\mathit{\text{scene-level}}$ priors for efficient hypothesis initialization; (2) $\mathit{\text{object-level}}\mathit{\text{symmetry}}$ priors for geometry completion via mirror fusion; and (3) $\mathit{\text{patch-level}}$ priors for discriminative refinement. Extensive experiments demonstrate that OneViewAll achieves $\mathbf{\text{92.5\%}}$ ADD-0.1 accuracy on the LINEMOD dataset using only one real reference view – significantly outperforming the CVPR 2025 baseline One2Any (52.6%). It also yields consistent improvements on YCB-V, Real275, and Toyota-Light while maintaining low inference latency. Our results underscore the efficacy of symmetry-aware projection in handling symmetric, texture-less, and occluded objects.