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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.13060 |
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| _version_ | 1866917988781785088 |
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| author | Goesele, Michael Andersen, Daniel Chen, Yujia Green, Simon Ilg, Eddy Li, Chao Liu, Johnson Kuo, Grace Wan, Logan Newcombe, Richard |
| author_facet | Goesele, Michael Andersen, Daniel Chen, Yujia Green, Simon Ilg, Eddy Li, Chao Liu, Johnson Kuo, Grace Wan, Logan Newcombe, Richard |
| contents | In recent years smart glasses technology has rapidly advanced, opening up entirely new areas for mobile computing. We expect future smart glasses will need to be all-day wearable, adopting a small form factor to meet the requirements of volume, weight, fashionability and social acceptability, which puts significant constraints on the space of possible solutions. Additional challenges arise due to the fact that smart glasses are worn in arbitrary environments while their wearer moves and performs everyday activities. In this paper, we systematically analyze the space of imaging from smart glasses and derive several fundamental limits that govern this imaging domain. We discuss the impact of these limits on achievable image quality and camera module size -- comparing in particular to related devices such as mobile phones. We then propose a novel distributed imaging approach that allows to minimize the size of the individual camera modules when compared to a standard monolithic camera design. Finally, we demonstrate the properties of this novel approach in a series of experiments using synthetic data as well as images captured with two different prototype implementations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13060 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Imaging for All-Day Wearable Smart Glasses Goesele, Michael Andersen, Daniel Chen, Yujia Green, Simon Ilg, Eddy Li, Chao Liu, Johnson Kuo, Grace Wan, Logan Newcombe, Richard Computer Vision and Pattern Recognition In recent years smart glasses technology has rapidly advanced, opening up entirely new areas for mobile computing. We expect future smart glasses will need to be all-day wearable, adopting a small form factor to meet the requirements of volume, weight, fashionability and social acceptability, which puts significant constraints on the space of possible solutions. Additional challenges arise due to the fact that smart glasses are worn in arbitrary environments while their wearer moves and performs everyday activities. In this paper, we systematically analyze the space of imaging from smart glasses and derive several fundamental limits that govern this imaging domain. We discuss the impact of these limits on achievable image quality and camera module size -- comparing in particular to related devices such as mobile phones. We then propose a novel distributed imaging approach that allows to minimize the size of the individual camera modules when compared to a standard monolithic camera design. Finally, we demonstrate the properties of this novel approach in a series of experiments using synthetic data as well as images captured with two different prototype implementations. |
| title | Imaging for All-Day Wearable Smart Glasses |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.13060 |