BiasBench: A reproducible benchmark for tuning the biases of event cameras
This addresses a bottleneck for researchers and practitioners in computer vision and robotics using event cameras, though it is incremental as it builds on existing event camera technology.
The paper tackles the problem of tuning biases in event-based cameras, which lack systematic configuration tools, by introducing BiasBench, a reproducible dataset with multiple scenes and quality metrics, and an RL-based method for online bias adjustments.
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.