The EventCV Library for Event-Based Robotic Vision

2026-09-18

AI Summary

本文介绍EventCV库，通过提供一系列处理事件相机数据的功能来简化其在机器人视觉中的应用，解决现有工具兼容性和效率问题。

Abstract

Event cameras detect per-pixel brightness changes asynchronously on microsecond timescales, with high dynamic range and low power draw. These are desirable properties for robots that move fast or work in difficult lighting conditions. However, integrating an event camera into a real-world robotic pipeline still requires substantial effort: plug-and-play drivers do not exist, event streams are recorded in a variety of incompatible file formats, and most projects rely on custom research-grade code. Here, we present EventCV, an open-source and extensible Rust library with OpenCV-style Python bindings that lowers the entry barrier to working with event cameras. EventCV provides a wide range of features: denoising filters and geometric transforms, augmentations, corner detection and unsupervised feature learning, contrast-maximization motion estimation, a video-to-events simulator, and Open Neural Network Exchange (ONNX) inference for deployment in robotic stacks. EventCV integrates the Neuromorphic Drivers package, allowing an event camera stream to be processed directly in real time. No existing toolkit covers this range of operations in one package, and EventCV builds representations and decodes files 1.1x to 3.7x faster than the currently available libraries. We deploy EventCV on a Jetson Orin AGX and present three robotics case studies spanning object detection, on-device model inference, and localization. Project webpage: https://eventcv.net.

Problem

• event camera
• robotic pipeline
• plug-and-play drivers
• file formats
• custom research-grade code

Innovation

• Event Camera
• Real-time Processing
• Rust Library
• Robotics Vision
• ONNX Inference