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arXiv · 2305.17934

ZeroPose: CAD-Prompted Zero-shot Object 6D Pose Estimation in Cluttered Scenes

Jianqiu Chen·Zikun Zhou·Mingshan Sun·Tianpeng Bao·Rui Zhao·Liwei Wu·Zhenyu He
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Abstract

Many robotics and industry applications have a high demand for the capability to estimate the 6D pose of novel objects from the cluttered scene. However, existing classic pose estimation methods are object-specific, which can only handle the specific objects seen during training. When applied to a novel object, these methods necessitate a cumbersome onboarding process, which involves extensive dataset preparation and model retraining. The extensive duration and resource consumption of onboarding limit their practicality in real-world applications. In this paper, we introduce ZeroPose, a novel zero-shot framework that performs pose estimation following a Discovery-Orientation-Registration (DOR) inference pipeline. This framework generalizes to novel objects without requiring model retraining. Given the CAD model of a novel object, ZeroPose enables in seconds onboarding time to extract visual and geometric embeddings from the CAD model as a prompt. With the prompting of the above embeddings, DOR can discover all related instances and estimate their 6D poses without additional human interaction or presupposing scene conditions. Compared with existing zero-shot methods solved by the render-and-compare paradigm, the DOR pipeline formulates the object pose estimation into a feature-matching problem, which avoids time-consuming online rendering and improves efficiency. Experimental results on the seven datasets show that ZeroPose as a zero-shot method achieves comparable performance with object-specific training methods and outperforms the state-of-the-art zero-shot method with 50x inference speed improvement.

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Jianqiu Chen, Zikun Zhou, Mingshan Sun, Tianpeng Bao, Rui Zhao, Liwei Wu, Zhenyu He. 2023-05-29. ZeroPose: CAD-Prompted Zero-shot Object 6D Pose Estimation in Cluttered Scenes. https://arxiv.org/abs/2305.17934

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