One View, Many Worlds: Single-Image to 3D Object Meets Generative Domain Randomization for One-Shot 6D Pose Estimation

• URL: http://arxiv.org/abs/2509.07978v1
• Date: Tue, 09 Sep 2025 17:59:02 GMT
• Title: One View, Many Worlds: Single-Image to 3D Object Meets Generative Domain Randomization for One-Shot 6D Pose Estimation
• Authors: Zheng Geng, Nan Wang, Shaocong Xu, Chongjie Ye, Bohan Li, Zhaoxi Chen, Sida Peng, Hao Zhao,
• Abstract summary: Estimating the 6D pose of arbitrary unseen objects from a single reference image is critical for robotics operating in the long-tail of real-world instances.<n>We propose OnePoseViaGen, a pipeline that tackles these challenges through two key components.<n>We demonstrate robust dexterous grasping with a real robot hand, validating the practicality of our method in real-world manipulation.
• Score: 32.45730375971019
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Estimating the 6D pose of arbitrary unseen objects from a single reference image is critical for robotics operating in the long-tail of real-world instances. However, this setting is notoriously challenging: 3D models are rarely available, single-view reconstructions lack metric scale, and domain gaps between generated models and real-world images undermine robustness. We propose OnePoseViaGen, a pipeline that tackles these challenges through two key components. First, a coarse-to-fine alignment module jointly refines scale and pose by combining multi-view feature matching with render-and-compare refinement. Second, a text-guided generative domain randomization strategy diversifies textures, enabling effective fine-tuning of pose estimators with synthetic data. Together, these steps allow high-fidelity single-view 3D generation to support reliable one-shot 6D pose estimation. On challenging benchmarks (YCBInEOAT, Toyota-Light, LM-O), OnePoseViaGen achieves state-of-the-art performance far surpassing prior approaches. We further demonstrate robust dexterous grasping with a real robot hand, validating the practicality of our method in real-world manipulation. Project page: https://gzwsama.github.io/OnePoseviaGen.github.io/

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