Pose Anything Anywhere:Model-free Object Poses from Arbitrary References

• URL: http://arxiv.org/abs/2606.23634v1
• Date: Mon, 22 Jun 2026 17:23:57 GMT
• Title: Pose Anything Anywhere:Model-free Object Poses from Arbitrary References
• Abstract summary: We present PANY, a model-free framework that seamlessly supports both RGB and RGB-D inputs.<n>It operates on one or sparse pose-free reference views, and generalizes effectively to novel objects.<n>It achieves state-of-the-art performance across multiple benchmarks, substantially outperforming existing model-free methods.
• Score: 64.58017857473774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating the 6D pose of unseen objects is a fundamental yet challenging problem for open-world robotics and embodied perception. Model-based methods are accurate but depend on CAD assets or heavy onboarding, while most model-free approaches are still limited to pairwise single-anchor matching and thus fail under occlusion and large viewpoint changes with low query-reference overlap. Therefore, we present PANY, a unified model-free framework that seamlessly supports both RGB and RGB-D inputs, operates on one or sparse pose-free reference views, and generalizes effectively to novel objects. Built on a multi-view transformer geometry backbone, PANY moves beyond pairwise matching by learning view-consistent geometry and cross-view alignment cues that remain stable under wide baselines and limited overlap. When additional unposed assist views are available, PANY aggregates them via pose-graph canonical registration to increase geometric coverage and reinforce the final pose. Extensive experiments show that PANY achieves state-of-the-art performance across multiple benchmarks, substantially outperforming existing model-free methods, improving pose accuracy by +12% on YCB-V and over +20% on LM-O. Furthermore, PANY consistently performs well under both single-reference and sparse-reference settings, demonstrating strong robustness in real-world environments.

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