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.

本研究提出了一种新的密集对应方法，用于从单个RGB-D图像估计对象的六自由度姿态。与现有的数据驱动方法相比，该方法通过使用分层二进制曲面编码以粗略到精细的方式建立3D-3D对应关系，并在表面上采用点到曲面匹配和迭代地收缩曲面直至成为对应点，逐渐去除异常值。在公共基准测试集LM-O、YCB-V和T-Less上进行了大量实验，结果显示我们的方法超越了所有无精炼方法，并且与高成本的精炼方法相媲美。关键是，我们的方法计算效率高，可以在具有高准确性要求的实时关键应用中使用。代码和模型将被发布。

HiPose: RGB-D 6DoF物体姿态估计的分层二进制表面编码与对应关系剪枝