Most existing learning-based pose estimation methods are typically developed for non-zero-shot scenarios, where they can only estimate the poses of objects present in the training dataset. This setting restricts their applicability to unseen objects in the training phase. In this paper, we introduce a fully zero-shot pose estimation pipeline that leverages the 3D models of objects as clues. Specifically, we design a two-step pipeline consisting of 3D model-based zero-shot instance segmentation and a zero-shot pose estimator. For the first step, there is a novel way to perform zero-shot instance segmentation based on the 3D models instead of text descriptions, which can handle complex properties of unseen objects. For the second step, we utilize a hierarchical geometric structure matching mechanism to perform zero-shot pose estimation which is 10 times faster than the current render-based method. Extensive experimental results on the seven core datasets on the BOP challenge show that the proposed method outperforms the zero-shot state-of-the-art method with higher speed and lower computation cost.

翻译：大多数现有的基于学习的姿态估计方法通常针对非零样本场景开发，只能估计训练数据集中存在的物体姿态。这种设置限制了其在训练阶段未见物体上的适用性。本文提出了一种完全零样本的姿态估计流程，利用物体的3D模型作为线索。具体而言，我们设计了一个两步流程，包括基于3D模型的零样本实例分割和零样本姿态估计器。第一步，我们提出了一种基于3D模型而非文本描述进行零样本实例分割的新方法，可处理未见物体的复杂属性。第二步，我们利用层级几何结构匹配机制进行零样本姿态估计，其速度比当前基于渲染的方法快10倍。在BOP挑战的七个核心数据集上的大量实验结果表明，所提方法以更高速度和更低计算成本优于现有零样本最先进方法。