用参考图和几何对应关系，精准估计未知物体的6D姿态。
RefPose: Leveraging Reference Geometric Correspondences for Accurate 6D Pose Estimation of Unseen Objects
- 通过参考图像生成初始姿态，利用渲染对比迭代优化。
- 在BOP基准上达到当前最优性能，且推理速度较快。
- 无需预设模型，可动态适应新物体，适合未知物体识别。
从单目RGB图像中估计未见过物体的6D姿态仍是挑战，尤其缺乏物体特定先验知识。为此，我们提出RefPose，一种利用参考图像和几何对应关系进行引导的新方法。RefPose首先通过物体模板渲染参考图像，并建立几何对应关系以获得初始姿态；在精炼阶段，基于生成的参考图像估计查询图像的几何对应关系，并通过渲染-对比迭代优化姿态。为提升估计精度，引入相关体积引导注意力机制，有效捕捉查询与参考图像间的关联。不同于依赖预定义物体模型的传统方法，RefPose通过参考图像和几何对应关系动态适应新物体形状，从而在未见过物体上表现稳健。在BOP基准数据集上的大量实验表明，RefPose实现了当前最优结果，同时保持了具有竞争力的运行效率。
原文摘要 · Abstract (English)
Estimating the 6D pose of unseen objects from monocular RGB images remains a challenging problem, especially due to the lack of prior object-specific knowledge. To tackle this issue, we propose RefPose, an innovative approach to object pose estimation that leverages a reference image and geometric correspondence as guidance. RefPose first predicts an initial pose by using object templates to render the reference image and establish the geometric correspondence needed for the refinement stage. During the refinement stage, RefPose estimates the geometric correspondence of the query based on the generated references and iteratively refines the pose through a render-and-compare approach. To enhance this estimation, we introduce a correlation volume-guided attention mechanism that effectively captures correlations between the query and reference images. Unlike traditional methods that depend on pre-defined object models, RefPose dynamically adapts to new object shapes by leveraging a reference image and geometric correspondence. This results in robust performance across previously unseen objects. Extensive evaluation on the BOP benchmark datasets shows that RefPose achieves state-of-the-art results while maintaining a competitive runtime.
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