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Hough-space-based hypothesis generation and hypothesis verification for 3D object recognition and 6D pose estimation

Zhou, Wei and Ma, Caiwen and Kuijper, Arjan (2018):
Hough-space-based hypothesis generation and hypothesis verification for 3D object recognition and 6D pose estimation.
In: Computers & Graphics, pp. 122-134, 72, ISSN 00978493,
DOI: 10.1016/j.cag.2018.01.011,
[Online-Edition: https://doi.org/10.1016/j.cag.2018.01.011],
[Article]

Abstract

Hypothesis Generation (HG) and Hypothesis Verification (HV) play an important role in 3D objection recognition. However, performing 3D object recognition in cluttered scenes using HG and HV still re- mains a largely unsolved problem. High False Positive (FP) in HG and HV stages are witnessed due to clutter and occlusion, which will further affect the final accuracy of recognition. To address these prob- lems, we propose a novel Hough-space-based HG approach for extracting hypotheses. Differently from the existing methods, our approach is based on a Hough space which adopts a self-adapted measure to generate hypotheses. Based on this, a novel HV-based method is proposed to verify the hypotheses obtained from HG procedures. The proposed method is evaluated on four public benchmark datasets to verify its performance. Experiments show that our approach outperforms state-of-the-art methods, and obtains a higher recognition rate without sacrificing precision both at high FP rates and high occlusion rates.

Item Type: Article
Erschienen: 2018
Creators: Zhou, Wei and Ma, Caiwen and Kuijper, Arjan
Title: Hough-space-based hypothesis generation and hypothesis verification for 3D object recognition and 6D pose estimation
Language: English
Abstract:

Hypothesis Generation (HG) and Hypothesis Verification (HV) play an important role in 3D objection recognition. However, performing 3D object recognition in cluttered scenes using HG and HV still re- mains a largely unsolved problem. High False Positive (FP) in HG and HV stages are witnessed due to clutter and occlusion, which will further affect the final accuracy of recognition. To address these prob- lems, we propose a novel Hough-space-based HG approach for extracting hypotheses. Differently from the existing methods, our approach is based on a Hough space which adopts a self-adapted measure to generate hypotheses. Based on this, a novel HV-based method is proposed to verify the hypotheses obtained from HG procedures. The proposed method is evaluated on four public benchmark datasets to verify its performance. Experiments show that our approach outperforms state-of-the-art methods, and obtains a higher recognition rate without sacrificing precision both at high FP rates and high occlusion rates.

Journal or Publication Title: Computers & Graphics
Volume: 72
Uncontrolled Keywords: Verification, Object recognition
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 01 Jul 2019 08:35
DOI: 10.1016/j.cag.2018.01.011
Official URL: https://doi.org/10.1016/j.cag.2018.01.011
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