Model-Based Segmentation-Supported Camera Tracking in Fab’s Indoor Environments
Currently, it is a norm to design a semiconductor fab using building information models (BIMs), which refer to a digital representation of a building’s physical and functional characteristics. The comprehensive data provided by BIMs include 3D geometric models. This paper presents a 3D mo...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10596301/ |
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| author | Jeonghyeon Ahn Jungho Ha Jaemin Son Junghyun Han |
| author_facet | Jeonghyeon Ahn Jungho Ha Jaemin Son Junghyun Han |
| author_sort | Jeonghyeon Ahn |
| collection | DOAJ |
| description | Currently, it is a norm to design a semiconductor fab using building information models (BIMs), which refer to a digital representation of a building’s physical and functional characteristics. The comprehensive data provided by BIMs include 3D geometric models. This paper presents a 3D model-based camera tracking method, which is targeted at navigating a fab’s wide indoor environment. The key observation made in designing the method is that there are a number of fixed objects in such an indoor environment. The columns are the representative among them. Our method extracts the columns from the input image and matches them to their BIMs to estimate the camera pose. The estimation accuracy is significantly increased by adopting an instance segmentation network. It is trained with a dataset, which is extracted from the target indoor environment and processed by our own data engine. The test results show that our tracking method is drift-free, accurate and robust. We envision that it can be used in many applications such as AR-based visual inspection. |
| format | Article |
| id | doaj-art-4c170ad965bd4fbdafc99f1563ab1778 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4c170ad965bd4fbdafc99f1563ab17782025-08-20T03:13:40ZengIEEEIEEE Access2169-35362024-01-0112969119692310.1109/ACCESS.2024.342737810596301Model-Based Segmentation-Supported Camera Tracking in Fab’s Indoor EnvironmentsJeonghyeon Ahn0https://orcid.org/0009-0009-3761-9337Jungho Ha1https://orcid.org/0009-0009-6901-4957Jaemin Son2https://orcid.org/0009-0008-3764-0243Junghyun Han3https://orcid.org/0000-0001-6438-2974Department of Computer Science and Engineering, Korea University, Seoul, South KoreaDepartment of Computer Science and Engineering, Korea University, Seoul, South KoreaDepartment of Computer Science and Engineering, Korea University, Seoul, South KoreaDepartment of Computer Science and Engineering, Korea University, Seoul, South KoreaCurrently, it is a norm to design a semiconductor fab using building information models (BIMs), which refer to a digital representation of a building’s physical and functional characteristics. The comprehensive data provided by BIMs include 3D geometric models. This paper presents a 3D model-based camera tracking method, which is targeted at navigating a fab’s wide indoor environment. The key observation made in designing the method is that there are a number of fixed objects in such an indoor environment. The columns are the representative among them. Our method extracts the columns from the input image and matches them to their BIMs to estimate the camera pose. The estimation accuracy is significantly increased by adopting an instance segmentation network. It is trained with a dataset, which is extracted from the target indoor environment and processed by our own data engine. The test results show that our tracking method is drift-free, accurate and robust. We envision that it can be used in many applications such as AR-based visual inspection.https://ieeexplore.ieee.org/document/10596301/Augmented realitybuilding information modelingcamera trackinginstance segmentation |
| spellingShingle | Jeonghyeon Ahn Jungho Ha Jaemin Son Junghyun Han Model-Based Segmentation-Supported Camera Tracking in Fab’s Indoor Environments IEEE Access Augmented reality building information modeling camera tracking instance segmentation |
| title | Model-Based Segmentation-Supported Camera Tracking in Fab’s Indoor Environments |
| title_full | Model-Based Segmentation-Supported Camera Tracking in Fab’s Indoor Environments |
| title_fullStr | Model-Based Segmentation-Supported Camera Tracking in Fab’s Indoor Environments |
| title_full_unstemmed | Model-Based Segmentation-Supported Camera Tracking in Fab’s Indoor Environments |
| title_short | Model-Based Segmentation-Supported Camera Tracking in Fab’s Indoor Environments |
| title_sort | model based segmentation supported camera tracking in fab x2019 s indoor environments |
| topic | Augmented reality building information modeling camera tracking instance segmentation |
| url | https://ieeexplore.ieee.org/document/10596301/ |
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