实时可视化事件视频转码，提升神经形态视觉处理效率
adder-viz: Real-Time Visualization Software for Transcoding Event Video
- 基于统一的ADDER表示法，实现事件视频高效转码
- 支持实时可视化，显著提升处理速度与灵活性
- 开源工具适合研究者快速部署与验证新算法
近年来，神经形态事件视频研究迅速发展，主要面向计算机视觉应用。事件视频不依赖传统帧，而是以异步、逐像素的强度采样方式记录信息。尽管已有研究针对特定事件相机提出多种表示方法，但这些方法在灵活性、速度和可压缩性方面仍存在局限。我们此前提出的统一ADDER表示法旨在解决这些问题。本文进一步改进了adder-viz软件，实现了事件视频转码过程的实时可视化，并支持应用闭环。该MIT许可的软件已发布于https://github.com/ac-freeman/adder-codec-rs，便于广泛使用。
原文摘要 · Abstract (English)
Recent years have brought about a surge in neuromorphic ``event'' video research, primarily targeting computer vision applications. Event video eschews video frames in favor of asynchronous, per-pixel intensity samples. While much work has focused on a handful of representations for specific event cameras, these representations have shown limitations in flexibility, speed, and compressibility. We previously proposed the unified ADDER representation to address these concerns. This paper introduces numerous improvements to the adder-viz software for visualizing real-time event transcode processes and applications in-the-loop. The MIT-licensed software is available from a centralized repository at https://github.com/ac-freeman/adder-codec-rs.
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