  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.
