Estimating 6D Aircraft Pose from Keypoints and Structures
This article addresses the challenge of 6D aircraft pose estimation from a single RGB image during the flight. Many recent works have shown that keypoints-based approaches, which first detect keypoints and then estimate the 6D pose, achieve remarkable performance. However, it is hard to locate the k...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-02-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/4/663 |
| _version_ | 1797396824948998144 |
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| author | Runze Fan Ting-Bing Xu Zhenzhong Wei |
| author_facet | Runze Fan Ting-Bing Xu Zhenzhong Wei |
| author_sort | Runze Fan |
| collection | DOAJ |
| description | This article addresses the challenge of 6D aircraft pose estimation from a single RGB image during the flight. Many recent works have shown that keypoints-based approaches, which first detect keypoints and then estimate the 6D pose, achieve remarkable performance. However, it is hard to locate the keypoints precisely in complex weather scenes. In this article, we propose a novel approach, called Pose Estimation with Keypoints and Structures (PEKS), which leverages multiple intermediate representations to estimate the 6D pose. Unlike previous works, our approach simultaneously locates keypoints and structures to recover the pose parameter of aircraft through a Perspective-n-Point Structure (PnPS) algorithm. These representations integrate the local geometric information of the object and the topological relationship between components of the target, which effectively improve the accuracy and robustness of 6D pose estimation. In addition, we contribute a dataset for aircraft pose estimation which consists of 3681 real images and 216,000 rendered images. Extensive experiments on our own aircraft pose dataset and multiple open-access pose datasets (e.g., ObjectNet3D, LineMOD) demonstrate that our proposed method can accurately estimate 6D aircraft pose in various complex weather scenes while achieving the comparative performance with the state-of-the-art pose estimation methods. |
| first_indexed | 2024-03-09T00:57:07Z |
| format | Article |
| id | doaj.art-f717331925c74d0db4547522d7c9164c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T00:57:07Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f717331925c74d0db4547522d7c9164c2023-12-11T16:52:05ZengMDPI AGRemote Sensing2072-42922021-02-0113466310.3390/rs13040663Estimating 6D Aircraft Pose from Keypoints and StructuresRunze Fan0Ting-Bing Xu1Zhenzhong Wei2Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, ChinaKey Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, ChinaKey Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, ChinaThis article addresses the challenge of 6D aircraft pose estimation from a single RGB image during the flight. Many recent works have shown that keypoints-based approaches, which first detect keypoints and then estimate the 6D pose, achieve remarkable performance. However, it is hard to locate the keypoints precisely in complex weather scenes. In this article, we propose a novel approach, called Pose Estimation with Keypoints and Structures (PEKS), which leverages multiple intermediate representations to estimate the 6D pose. Unlike previous works, our approach simultaneously locates keypoints and structures to recover the pose parameter of aircraft through a Perspective-n-Point Structure (PnPS) algorithm. These representations integrate the local geometric information of the object and the topological relationship between components of the target, which effectively improve the accuracy and robustness of 6D pose estimation. In addition, we contribute a dataset for aircraft pose estimation which consists of 3681 real images and 216,000 rendered images. Extensive experiments on our own aircraft pose dataset and multiple open-access pose datasets (e.g., ObjectNet3D, LineMOD) demonstrate that our proposed method can accurately estimate 6D aircraft pose in various complex weather scenes while achieving the comparative performance with the state-of-the-art pose estimation methods.https://www.mdpi.com/2072-4292/13/4/6636D aircraft pose estimationkeypointsstructuresPnPS algorithm |
| spellingShingle | Runze Fan Ting-Bing Xu Zhenzhong Wei Estimating 6D Aircraft Pose from Keypoints and Structures Remote Sensing 6D aircraft pose estimation keypoints structures PnPS algorithm |
| title | Estimating 6D Aircraft Pose from Keypoints and Structures |
| title_full | Estimating 6D Aircraft Pose from Keypoints and Structures |
| title_fullStr | Estimating 6D Aircraft Pose from Keypoints and Structures |
| title_full_unstemmed | Estimating 6D Aircraft Pose from Keypoints and Structures |
| title_short | Estimating 6D Aircraft Pose from Keypoints and Structures |
| title_sort | estimating 6d aircraft pose from keypoints and structures |
| topic | 6D aircraft pose estimation keypoints structures PnPS algorithm |
| url | https://www.mdpi.com/2072-4292/13/4/663 |
| work_keys_str_mv | AT runzefan estimating6daircraftposefromkeypointsandstructures AT tingbingxu estimating6daircraftposefromkeypointsandstructures AT zhenzhongwei estimating6daircraftposefromkeypointsandstructures |