Object pose refinement is essential for robust object pose estimation. Previous work has made significant progress towards instance-level object pose refinement. Yet, category-level pose refinement is a more challenging problem due to large shape variations within a category and the discrepancies between the target object and the shape prior. To address these challenges, we introduce a novel architecture for category-level object pose refinement. Our approach integrates an HS-layer and learnable affine transformations, which aims to enhance the extraction and alignment of geometric information. Additionally, we introduce a cross-cloud transformation mechanism that efficiently merges diverse data sources. Finally, we push the limits of our model by incorporating the shape prior information for translation and size error prediction. We conducted extensive experiments to demonstrate the effectiveness of the proposed framework. Through extensive quantitative experiments, we demonstrate significant improvement over the baseline method by a large margin across all metrics.

本研究提出了一种新的框架用于类别级目标姿态细化，通过整合HS层和可学习仿射变换来增强几何信息的提取和对齐，同时引入了跨云变换机制来高效合并多样化数据源，并在翻译和尺寸误差预测中应用形状先验信息，通过大量定量实验证明了所提出框架在所有评测指标上相对于基线方法的显著提高。

GeoReF: 类别级别物体位姿细化的形状变化间的几何对齐