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

• URL: http://arxiv.org/abs/2605.07023v1
• Date: Thu, 07 May 2026 23:11:29 GMT
• Title: OneViewAll: Semantic Prior Guided One-View 6D Pose Estimation for Novel Objects
• Abstract summary: 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.<n>We propose textbfOneViewAll, a semantic-prior-guided framework that performs pose estimation via a novel Project-and-Compare paradigm.
• Score: 31.001161060131622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 \textbf{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) \textit{category- and scene-level} priors for efficient hypothesis initialization; (2) \textit{object-level symmetry} priors for geometry completion via mirror fusion; and (3) \textit{patch-level} priors for discriminative refinement. Extensive experiments demonstrate that OneViewAll achieves \textbf{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.

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