In this work, we present a novel dense-correspondence method for 6DoF object pose estimation from a single RGB-D image. While many existing data-driven methods achieve impressive performance, they tend to be time-consuming due to their reliance on rendering-based refinement approaches. To circumvent this limitation, we present HiPose, which establishes 3D-3D correspondences in a coarse-to-fine manner with a hierarchical binary surface encoding. Unlike previous dense-correspondence methods, we estimate the correspondence surface by employing point-to-surface matching and iteratively constricting the surface until it becomes a correspondence point while gradually removing outliers. Extensive experiments on public benchmarks LM-O, YCB-V, and T-Less demonstrate that our method surpasses all refinement-free methods and is even on par with expensive refinement-based approaches. Crucially, our approach is computationally efficient and enables real-time critical applications with high accuracy requirements. Code and models will be released.

翻译：本文提出了一种新颖的稠密对应关系方法，用于从单张RGB-D图像中估计物体的6DoF姿态。现有许多数据驱动方法虽取得了显著性能，但由于依赖基于渲染的细化策略，往往计算耗时。为克服这一局限，我们提出了HiPose方法，通过层级二值表面编码以从粗到精的方式建立3D-3D对应关系。与以往稠密对应方法不同，我们采用点对面匹配估计对应表面，通过迭代收缩表面直至其收敛为对应点，同时逐步剔除离群点。在公开基准LM-O、YCB-V和T-Less上的大量实验表明，我们的方法超越所有无需细化的方法，甚至与昂贵的基于细化的方法性能相当。关键在于，本方法计算高效，能够支持对精度要求严苛的实时关键应用。代码与模型将公开。