NeRF-To-Real Tester: Neural Radiance Fields as Test Image Generators for Vision of Autonomous Systems
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910946671198208 |
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| author | Weihl, Laura Wehbe, Bilal Wąsowski, Andrzej |
| author_facet | Weihl, Laura Wehbe, Bilal Wąsowski, Andrzej |
| contents | Autonomous inspection of infrastructure on land and in water is a quickly growing market, with applications including surveying constructions, monitoring plants, and tracking environmental changes in on- and off-shore wind energy farms. For Autonomous Underwater Vehicles and Unmanned Aerial Vehicles overfitting of controllers to simulation conditions fundamentally leads to poor performance in the operation environment. There is a pressing need for more diverse and realistic test data that accurately represents the challenges faced by these systems. We address the challenge of generating perception test data for autonomous systems by leveraging Neural Radiance Fields to generate realistic and diverse test images, and integrating them into a metamorphic testing framework for vision components such as vSLAM and object detection. Our tool, N2R-Tester, allows training models of custom scenes and rendering test images from perturbed positions. An experimental evaluation of N2R-Tester on eight different vision components in AUVs and UAVs demonstrates the efficacy and versatility of the approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_16141 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NeRF-To-Real Tester: Neural Radiance Fields as Test Image Generators for Vision of Autonomous Systems Weihl, Laura Wehbe, Bilal Wąsowski, Andrzej Computer Vision and Pattern Recognition Autonomous inspection of infrastructure on land and in water is a quickly growing market, with applications including surveying constructions, monitoring plants, and tracking environmental changes in on- and off-shore wind energy farms. For Autonomous Underwater Vehicles and Unmanned Aerial Vehicles overfitting of controllers to simulation conditions fundamentally leads to poor performance in the operation environment. There is a pressing need for more diverse and realistic test data that accurately represents the challenges faced by these systems. We address the challenge of generating perception test data for autonomous systems by leveraging Neural Radiance Fields to generate realistic and diverse test images, and integrating them into a metamorphic testing framework for vision components such as vSLAM and object detection. Our tool, N2R-Tester, allows training models of custom scenes and rendering test images from perturbed positions. An experimental evaluation of N2R-Tester on eight different vision components in AUVs and UAVs demonstrates the efficacy and versatility of the approach. |
| title | NeRF-To-Real Tester: Neural Radiance Fields as Test Image Generators for Vision of Autonomous Systems |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.16141 |