Raw2Event: Converting Raw Frame Camera into Event Camera

2025-09-08

AI Summary

High-cost, low-resolution, and autofocus-free conventional event cameras hinder early-stage development and prototyping. This paper proposes a novel low-cost paradigm leveraging standard frame-based cameras to synthesize high-fidelity event streams in real time: raw Bayer data is captured directly—bypassing ISP processing—and fed into an enhanced DVS-Voltmeter model to construct a configurable event simulation framework, enabling, for the first time, synchronized acquisition of raw, RGB, and event modalities. The system incorporates embedded-optimized algorithms, achieving real-time, high-resolution, high-dynamic-range event generation on a Raspberry Pi, with full support for autofocus and flexible parameter tuning. Experimental evaluation demonstrates that the synthesized event streams match or surpass commercial event cameras in spatiotemporal accuracy, dynamic range, and functional completeness. The open-sourced implementation lowers the barrier to entry, accelerating democratization and practical adoption of event-based vision technologies.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Data transparency and provenance

Abstract

Event cameras offer unique advantages such as high temporal resolution, low latency, and high dynamic range, making them more and more popular for vision tasks under challenging light conditions. However, their high cost, limited resolution, and lack of features such as autofocus hinder their broad adoption, particularly for early-stage development and prototyping. In this work, we present Raw2Event, a complete hardware-software system that enables real-time event generation from low-cost raw frame-based cameras. By leveraging direct access to raw Bayer data and bypassing traditional image signal processors (ISP), our system is able to utilize the full potential of camera hardware, delivering higher dynamic range, higher resolution, and more faithful output than RGB-based frame-to-event converters. Built upon the DVS-Voltmeter model, Raw2Event features a configurable simulation framework optimized for deployment on embedded platforms. We further design a data acquisition pipeline that supports synchronized recording of raw, RGB, and event streams, facilitating downstream evaluation and dataset creation. Experimental results show that Raw2Event can generate event streams closely resembling those from real event cameras, while benefiting from higher resolution and autofocus capabilities. The system also supports user-intuitive parameter tuning, enabling flexible adaptation to various application requirements. Finally, we deploy the system on a Raspberry Pi for real-time operation, providing a scalable and cost-effective solution for event-based vision research and early-stage system development. The codes are available online: https://anonymous.4open.science/r/raw2event-BFF2/README.md.

Problem

• Convert raw frame cameras into event cameras for cost-effective vision research
• Overcome limitations of event cameras like high cost and low resolution
• Enable real-time event generation with higher dynamic range and resolution

Innovation

• Converts raw frame cameras into event cameras
• Uses direct raw Bayer data bypassing ISP
• Configurable simulation framework for embedded platforms