E-VLC: A Real-World Dataset for Event-based Visible Light Communication And Localization
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910919127203840 |
|---|---|
| author | Shiba, Shintaro Kong, Quan Kobori, Norimasa |
| author_facet | Shiba, Shintaro Kong, Quan Kobori, Norimasa |
| contents | Optical communication using modulated LEDs (e.g., visible light communication) is an emerging application for event cameras, thanks to their high spatio-temporal resolutions. Event cameras can be used simply to decode the LED signals and also to localize the camera relative to the LED marker positions. However, there is no public dataset to benchmark the decoding and localization in various real-world settings. We present, to the best of our knowledge, the first public dataset that consists of an event camera, a frame camera, and ground-truth poses that are precisely synchronized with hardware triggers. It provides various camera motions with various sensitivities in different scene brightness settings, both indoor and outdoor. Furthermore, we propose a novel method of localization that leverages the Contrast Maximization framework for motion estimation and compensation. The detailed analysis and experimental results demonstrate the advantages of LED-based localization with events over the conventional AR-marker--based one with frames, as well as the efficacy of the proposed method in localization. We hope that the proposed dataset serves as a future benchmark for both motion-related classical computer vision tasks and LED marker decoding tasks simultaneously, paving the way to broadening applications of event cameras on mobile devices. https://woven-visionai.github.io/evlc-dataset |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18521 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | E-VLC: A Real-World Dataset for Event-based Visible Light Communication And Localization Shiba, Shintaro Kong, Quan Kobori, Norimasa Computer Vision and Pattern Recognition Robotics Signal Processing Optical communication using modulated LEDs (e.g., visible light communication) is an emerging application for event cameras, thanks to their high spatio-temporal resolutions. Event cameras can be used simply to decode the LED signals and also to localize the camera relative to the LED marker positions. However, there is no public dataset to benchmark the decoding and localization in various real-world settings. We present, to the best of our knowledge, the first public dataset that consists of an event camera, a frame camera, and ground-truth poses that are precisely synchronized with hardware triggers. It provides various camera motions with various sensitivities in different scene brightness settings, both indoor and outdoor. Furthermore, we propose a novel method of localization that leverages the Contrast Maximization framework for motion estimation and compensation. The detailed analysis and experimental results demonstrate the advantages of LED-based localization with events over the conventional AR-marker--based one with frames, as well as the efficacy of the proposed method in localization. We hope that the proposed dataset serves as a future benchmark for both motion-related classical computer vision tasks and LED marker decoding tasks simultaneously, paving the way to broadening applications of event cameras on mobile devices. https://woven-visionai.github.io/evlc-dataset |
| title | E-VLC: A Real-World Dataset for Event-based Visible Light Communication And Localization |
| topic | Computer Vision and Pattern Recognition Robotics Signal Processing |
| url | https://arxiv.org/abs/2504.18521 |