# adder-viz: Real-Time Visualization Software for Transcoding Event Video

arXiv: https://arxiv.org/abs/2508.14996 · HTML: https://mlsanity.com/paper/2508.14996/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 20 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [End-to-End Learning of Representations for Asynchronous Event-Based Data](https://mlsanity.com/paper/1904.08245/) (2019)
- [Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars](https://mlsanity.com/paper/1804.01310/) (2018)
- [An Asynchronous Intensity Representation for Framed and Event Video Sources](https://mlsanity.com/paper/2301.08783/) (2023)
- [Accelerated Event-Based Feature Detection and Compression for Surveillance Video Systems](https://mlsanity.com/paper/2312.08213/) (2023)
- [Rethinking Video with a Universal Event-Based Representation](https://mlsanity.com/paper/2408.06248/) (2024)
- [An Open Software Suite for Event-Based Video](https://mlsanity.com/paper/2401.17151/) (2024)

## Abstract (arXiv)

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.