HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation

Yongliang Lin; Yongzhi Su; Praveen Nathan; Sandeep Inuganti; Yan Di; Martin Sundermeyer; Fabian Manhardt; Didier Stricker; Jason Rambach; Yu Zhang

arXiv · 2311.12588

HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation

Yongliang Lin·Yongzhi Su·Praveen Nathan·Sandeep Inuganti·Yan Di·Martin Sundermeyer·Fabian Manhardt·Didier Stricker·Jason Rambach·Yu Zhang

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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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BibTeX RIS

Yongliang Lin, Yongzhi Su, Praveen Nathan, Sandeep Inuganti, Yan Di, Martin Sundermeyer, Fabian Manhardt, Didier Stricker, Jason Rambach, Yu Zhang. 2023-11-21. HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation. https://arxiv.org/abs/2311.12588

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