A tool for calibrating event cameras by converting event data to images and using standard image-based calibration toolboxes.
E2Calib is a software pipeline for calibrating event cameras, which are sensors that output asynchronous brightness changes instead of full frames. It converts raw event data into reconstructed grayscale images, allowing researchers and engineers to use standard image-based calibration toolboxes to determine the camera's intrinsic and extrinsic parameters. This solves a key practical problem in deploying event cameras for applications requiring precise geometric measurements.
Computer vision researchers and robotics engineers working with event cameras who need to calibrate them for accurate 3D perception, sensor fusion, or SLAM systems.
Developers choose E2Calib because it provides a practical, open-source solution to a fundamental problem in event-based vision, supporting multiple event data formats and integrating seamlessly with established calibration workflows like Kalibr, without requiring proprietary tools.
CVPRW 2021: How to calibrate your event camera
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Supports converting event data from ROS bags, Pocolog/Rock files, and Prophesee raw/dat formats into a common HDF5 file, as outlined in the installation section, simplifying data handling for diverse sources.
Allows reconstruction at fixed frequencies for intrinsic calibration or at custom timestamps from triggers for extrinsic calibration, with upsampling to improve performance, detailed in the reconstruction options.
Provides scripts to output reconstructed images in formats compatible with popular calibration tools like Kalibr, including rosbag creation, enabling easy use with existing image-based pipelines.
Includes an upsampling rate parameter to process events at higher internal rates, which can fine-tune reconstruction performance, as explained in the reconstruction section to handle event density variations.
Installation is divided into separate environments for conversion and reconstruction, requiring specific tools like conda, Metavision 2.2 for Prophesee raw, and ROS dependencies, making setup non-trivial and error-prone.
Relies on the E2VID neural network for reconstruction, which demands significant GPU resources and limits real-time use, as it's designed for offline processing with high computational costs.
Only supports a limited set of event formats, and converting older Metavision versions requires additional steps to .dat files, potentially hindering compatibility with newer or proprietary sensor outputs.