构建可复现的事件相机偏置调优基准，提升低延迟视觉系统性能
BiasBench: A reproducible benchmark for tuning the biases of event cameras
- 设计网格化采样数据集，覆盖多场景不同偏置配置
- 引入强化学习方法实现在线偏置动态调整，提升下游任务精度
- 提供真实感仿真与质量评估指标，适配机器人和视觉算法研发
事件相机是一种仿生传感器，通过异步检测像素级光变化工作，具有高时间分辨率、低延迟和高动态范围等优势，广泛应用于计算机视觉与机器人领域。其输出质量高度依赖于称为‘偏置’的参数设置。尽管帧基相机已有成熟自动配置算法，但针对事件相机的偏置调优工具仍非常有限。由于事件相机仅在运动时生成事件，难以在相同场景下系统性测试不同偏置，而现有事件模拟器因无法准确反映电学电路与像素设计对偏置的影响，也不适合用于调优。为此，我们提出 BiasBench，一个可复现的事件数据集，包含三个不同场景，每个场景均以网格方式采样多种偏置组合，并为每组配置提供下游应用的质量度量。此外，我们提出一种基于强化学习的新方法，支持在线偏置调整。该框架可有效推动事件相机系统的自动化优化。
原文摘要 · Abstract (English)
Event-based cameras are bio-inspired sensors that detect light changes asynchronously for each pixel. They are increasingly used in fields like computer vision and robotics because of several advantages over traditional frame-based cameras, such as high temporal resolution, low latency, and high dynamic range. As with any camera, the output's quality depends on how well the camera's settings, called biases for event-based cameras, are configured. While frame-based cameras have advanced automatic configuration algorithms, there are very few such tools for tuning these biases. A systematic testing framework would require observing the same scene with different biases, which is tricky since event cameras only generate events when there is movement. Event simulators exist, but since biases heavily depend on the electrical circuit and the pixel design, available simulators are not well suited for bias tuning. To allow reproducibility, we present BiasBench, a novel event dataset containing multiple scenes with settings sampled in a grid-like pattern. We present three different scenes, each with a quality metric of the downstream application. Additionally, we present a novel, RL-based method to facilitate online bias adjustments.
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