解决机器人在未知物体形状材质下精准定位的难题
Category-Level and Open-Set Object Pose Estimation for Robotics
- 提出跨类别与开放集物体姿态估计的统一评估框架
- 发现现有方法在对称性与材质未知场景下精度显著下降
- 为提升泛化能力提供可落地的技术路线建议
物体姿态估计是计算机视觉与机器人领域的关键任务，广泛应用于场景理解与抓取。当目标物体的纹理、形状和尺寸部分或完全未知时，传统实例级方法难以适用。由于无法依赖纹理消除对称性歧义，6D姿态估计面临严峻挑战。本文系统比较了不同数据集、评估指标与算法在类别级姿态估计中的表现，分析了当前技术瓶颈，并提出从类别级向开放集泛化演进的可行路径，给出具有实际意义的改进方向。
原文摘要 · Abstract (English)
Object pose estimation enables a variety of tasks in computer vision and robotics, including scene understanding and robotic grasping. The complexity of a pose estimation task depends on the unknown variables related to the target object. While instance-level methods already excel for opaque and Lambertian objects, category-level and open-set methods, where texture, shape, and size are partially or entirely unknown, still struggle with these basic material properties. Since texture is unknown in these scenarios, it cannot be used for disambiguating object symmetries, another core challenge of 6D object pose estimation. The complexity of estimating 6D poses with such a manifold of unknowns led to various datasets, accuracy metrics, and algorithmic solutions. This paper compares datasets, accuracy metrics, and algorithms for solving 6D pose estimation on the category-level. Based on this comparison, we analyze how to bridge category-level and open-set object pose estimation to reach generalization and provide actionable recommendations.
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