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Research Article | Open Access

6DOF pose estimation of a 3D rigid object based on edge-enhanced point pair features

College of Computing, National University of Defense Technology, Changsha 410073, China
Department of Spine Surgery, the Second Xiangya Hospital, Central South University, Changsha 410011, China
Clinical Nursing Teaching and Research Section, the Second Xiangya Hospital, Changsha 410011, China
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
Beijing Institute of Tracking and Communication Technology, Beijing 100094, China
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Graphical Abstract

Abstract

The point pair feature (PPF) is widely used for 6D pose estimation. In this paper, we propose an efficient 6D pose estimation method based on the PPF framework. We introduce a well-targeted down-sampling strategy that focuses on edge areas for efficient feature extraction for complex geometry. A pose hypothesis validation approach is proposed to resolve ambiguity due to symmetry by calculating the edge matching degree. We perform evaluations on two challenging datasets and one real-world collected dataset, demonstrating the superiority of our method for pose estimation for geometrically complex, occluded, symmetrical objects. We further validate our method by applying it to simulated punctures.

Keywords

object recognitionpose estimation3D point cloudpoint pair feature (PPF)

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Computational Visual Media
Volume 10 Issue 1,
February 2024
Pages 61-77
DOI: 10.1007/s41095-022-0308-2
Cite this article:
Liu C, Chen F, Deng L, et al. 6DOF pose estimation of a 3D rigid object based on edge-enhanced point pair features. Computational Visual Media, 2024, 10(1): 61-77. https://doi.org/10.1007/s41095-022-0308-2

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Received: 12 May 2022
Accepted: 16 August 2022
Published: 30 November 2023
© The Author(s) 2023.

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