首个面向事件相机的可见光通信与定位真实世界数据集。
E-VLC: A Real-World Dataset for Event-based Visible Light Communication And Localization
- 构建同步硬件触发的多场景事件/帧图像数据集
- 事件信号定位精度优于传统AR标记方法
- 适合事件相机、智能设备定位研究者使用
基于调制LED的光学通信是事件相机的新兴应用，因其具备高时空分辨率。事件相机可直接解码LED信号，并实现相机相对于LED标记位置的定位。然而，目前缺乏公开数据集用于评估不同真实场景下的解码与定位性能。本文首次发布公开数据集，包含事件相机、帧相机及精确同步的硬件触发位姿数据，涵盖室内外多种光照条件和相机运动模式。此外，提出一种基于对比度最大化框架的新型定位方法，用于运动估计与补偿。详细分析与实验结果表明，基于事件的LED定位优于传统基于帧的AR标记方法，所提方法具有显著优势。该数据集有望成为未来运动相关经典计算机视觉任务与LED标记解码任务的统一基准，推动事件相机在移动设备上的广泛应用。
原文摘要 · Abstract (English)
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
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