Publication
HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation
Yongliang Lin; Yongzhi Su; Praveen Annamalai Nathan; Sandeep Prudhvi Krishna Inuganti; Yan Di; Martin Sundermayer; Fabian Manhardt; Didier Stricker; Jason Raphael Rambach; Yu Zhang
In: IEEE/CVF (Hrsg.). Proceedings of the. International Conference on Computer Vision and Pattern Recognition (CVPR-2024), June 17-21, Seattle, Washington, USA, IEEE/CVF, 2024.
Abstract
In this work, we present a novel dense-correspondence
method for 6DoF object pose estimation from a single RGBD
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.