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[bibtex]@InProceedings{Lin_2024_CVPR, author = {Lin, Yongliang and Su, Yongzhi and Nathan, Praveen and Inuganti, Sandeep and Di, Yan and Sundermeyer, Martin and Manhardt, Fabian and Stricker, Didier and Rambach, Jason and Zhang, Yu}, title = {HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {10148-10158} }
HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation
Abstract
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.
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