# Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensor

arXiv: https://arxiv.org/abs/1805.12326 · HTML: https://mlsanity.com/paper/1805.12326/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2018.

## Built on by (most cited first, 21 of 21)

- [Secrets of Event-Based Optical Flow](https://mlsanity.com/paper/2207.10022/) (2022)
- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [Event Collapse in Contrast Maximization Frameworks](https://mlsanity.com/paper/2207.04007/) (2022)
- [Globally-Optimal Contrast Maximisation for Event Cameras](https://mlsanity.com/paper/2206.05127/) (2022)
- [Real-Time Optical Flow for Vehicular Perception with Low- and High-Resolution Event Cameras](https://mlsanity.com/paper/2112.10591/) (2021)
- [Visual Odometry with an Event Camera Using Continuous Ray Warping and Volumetric Contrast Maximization](https://mlsanity.com/paper/2107.03011/) (2021)
- [Globally-Optimal Event Camera Motion Estimation](https://mlsanity.com/paper/2203.03914/) (2022)
- [Globally Optimal Event-Based Divergence Estimation for Ventral Landing](https://mlsanity.com/paper/2209.13168/) (2022)
- [ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation](https://mlsanity.com/paper/2203.11732/) (2022)
- [Event-based Motion Segmentation by Cascaded Two-Level Multi-Model Fitting](https://mlsanity.com/paper/2111.03483/) (2021)
- [EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation](https://mlsanity.com/paper/2506.03512/) (2025)
- [NeuroCLIP: Neuromorphic Data Understanding by CLIP and SNN](https://mlsanity.com/paper/2306.12073/) (2023)
- [Vector-Symbolic Architecture for Event-Based Optical Flow](https://mlsanity.com/paper/2405.08300/) (2024)
- [Motion Segmentation for Neuromorphic Aerial Surveillance](https://mlsanity.com/paper/2405.15209/) (2024)
- [Retina-Inspired Object Motion Segmentation for Event-Cameras](https://mlsanity.com/paper/2408.09454/) (2024)
- [Low-Latency Scalable Streaming for Event-Based Vision](https://mlsanity.com/paper/2412.07889/) (2024)
- [E-CHUM: Event-based Cameras for Human Detection and Urban Monitoring](https://mlsanity.com/paper/2512.11076/) (2025)
- [PCA Event-Based Optical Flow for Visual Odometry](https://mlsanity.com/paper/2105.03760/) (2021)
- [Real-Time Event-Based Tracking and Detection for Maritime Environments](https://mlsanity.com/paper/2202.04231/) (2022)
- [GNEP Based Dynamic Segmentation and Motion Estimation for Neuromorphic Imaging](https://mlsanity.com/paper/2307.02595/) (2023)
- [Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow](https://mlsanity.com/paper/2506.07878/) (2025)

## Abstract (arXiv)

We present an algorithm (SOFAS) to estimate the optical flow of events generated by a dynamic vision sensor (DVS). Where traditional cameras produce frames at a fixed rate, DVSs produce asynchronous events in response to intensity changes with a high temporal resolution. Our algorithm uses the fact that events are generated by edges in the scene to not only estimate the optical flow but also to simultaneously segment the image into objects which are travelling at the same velocity. This way it is able to avoid the aperture problem which affects other implementations such as Lucas-Kanade. Finally, we show that SOFAS produces more accurate results than traditional optic flow algorithms.