HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation
Creators
-
1.
Zhejiang University
-
2.
German Research Centre for Artificial Intelligence
-
3.
Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau
- 4. Technische Universitat Munchen
- 5. Google
Description
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.
Files
Lin_HiPose_Hierarchical_Binary_Surface_Encoding_and_Correspondence_Pruning_for_RGB-D_CVPR_2024_paper.pdf
Files
(6.1 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:5e4f83c6b65f3ca030b4ad003b44a523
|
6.1 MB | Preview Download |