# Real-Time Optical Flow for Vehicular Perception with Low- and High-Resolution Event Cameras

arXiv: https://arxiv.org/abs/2112.10591 · HTML: https://mlsanity.com/paper/2112.10591/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 54 references that are in the map.
- First posted on arXiv 20 Dec 2021.

## Builds on (most cited first, 24 of 24)

- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion](https://mlsanity.com/paper/1812.08156/) (2018)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [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)
- [UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss](https://mlsanity.com/paper/1711.07837/) (2017)
- [Learning to Detect Objects with a 1 Megapixel Event Camera](https://mlsanity.com/paper/2009.13436/) (2020)
- [A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation](https://mlsanity.com/paper/1804.01306/) (2018)
- [E-RAFT: Dense Optical Flow from Event Cameras](https://mlsanity.com/paper/2108.10552/) (2021)
- [Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy](https://mlsanity.com/paper/2009.08283/) (2020)
- [Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks](https://mlsanity.com/paper/2106.01862/) (2021)
- [Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks](https://mlsanity.com/paper/2003.06696/) (2020)
- [Continuous-time Intensity Estimation Using Event Cameras](https://mlsanity.com/paper/1811.00386/) (2018)
- [Scalable Scene Flow from Point Clouds in the Real World](https://mlsanity.com/paper/2103.01306/) (2021)
- [Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception](https://mlsanity.com/paper/1807.10936/) (2018)
- [Single Image Optical Flow Estimation with an Event Camera](https://mlsanity.com/paper/2004.00347/) (2020)
- [Globally Optimal Contrast Maximisation for Event-based Motion Estimation](https://mlsanity.com/paper/2002.10686/) (2020)
- [Distance Surface for Event-Based Optical Flow](https://mlsanity.com/paper/2003.12680/) (2020)
- [Real-time high speed motion prediction using fast aperture-robust event-driven visual flow](https://mlsanity.com/paper/1811.11135/) (2018)
- [Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensor](https://mlsanity.com/paper/1805.12326/) (2018)
- [Optical Flow Based Real-time Moving Object Detection in Unconstrained Scenes](https://mlsanity.com/paper/1807.04890/) (2018)

## Built on by (most cited first, 27 of 27)

- [Secrets of Event-Based Optical Flow](https://mlsanity.com/paper/2207.10022/) (2022)
- [Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow](https://mlsanity.com/paper/2303.05214/) (2023)
- [OpenESS: Event-based Semantic Scene Understanding with Open Vocabularies](https://mlsanity.com/paper/2405.05259/) (2024)
- [TMA: Temporal Motion Aggregation for Event-based Optical Flow](https://mlsanity.com/paper/2303.11629/) (2023)
- [PanoFlow: Learning 360deg Optical Flow for Surrounding Temporal Understanding](https://mlsanity.com/paper/2202.13388/) (2022)
- [Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation](https://mlsanity.com/paper/2407.10802/) (2024)
- [Emergent Visual Sensors for Autonomous Vehicles](https://mlsanity.com/paper/2205.09383/) (2022)
- [Enhancing Traffic Object Detection in Variable Illumination with RGB-Event Fusion](https://mlsanity.com/paper/2311.00436/) (2023)
- [Fast Event-based Optical Flow Estimation by Triplet Matching](https://mlsanity.com/paper/2212.12218/) (2022)
- [Neuromorphic Optical Flow and Real-time Implementation with Event Cameras](https://mlsanity.com/paper/2304.07139/) (2023)
- [Unsupervised Joint Learning of Optical Flow and Intensity with Event Cameras](https://mlsanity.com/paper/2503.17262/) (2025)
- [Neuromorphic High-Frequency 3D Dancing Pose Estimation in Dynamic Environment](https://mlsanity.com/paper/2301.06648/) (2023)
- [Secrets of Edge-Informed Contrast Maximization for Event-Based Vision](https://mlsanity.com/paper/2409.14611/) (2024)
- [EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation](https://mlsanity.com/paper/2506.03512/) (2025)
- [Learning Normal Flow Directly From Event Neighborhoods](https://mlsanity.com/paper/2412.11284/) (2024)
- [Vector-Symbolic Architecture for Event-Based Optical Flow](https://mlsanity.com/paper/2405.08300/) (2024)
- [Spatially-guided Temporal Aggregation for Robust Event-RGB Optical Flow Estimation](https://mlsanity.com/paper/2501.00838/) (2025)
- [Learning Efficient Meshflow and Optical Flow from Event Cameras](https://mlsanity.com/paper/2510.04111/) (2025)
- [ScaleFlow++: Robust and Accurate Estimation of 3D Motion from Video](https://mlsanity.com/paper/2407.09797/) (2024)
- [Event USKT : U-State Space Model in Knowledge Transfer for Event Cameras](https://mlsanity.com/paper/2411.15276/) (2024)
- [E-MoFlow: Learning Egomotion and Optical Flow from Event Data via Implicit Regularization](https://mlsanity.com/paper/2510.12753/) (2025)
- [Cross-modal Place Recognition in Image Databases using Event-based Sensors](https://mlsanity.com/paper/2307.01047/) (2023)
- [TETO: Tracking Events with Teacher Observation for Motion Estimation and Frame Interpolation](https://mlsanity.com/paper/2603.23487/) (2026)
- [Non-Uniform Exposure Imaging via Neuromorphic Shutter Control](https://mlsanity.com/paper/2404.13972/) (2024)
- [ScaleFlow++: Robust and Accurate Estimation of 3D Motion from Video](https://mlsanity.com/paper/2409.12202/) (2024)
- [Nonlinear Motion-Guided and Spatio-Temporal Aware Network for Unsupervised Event-Based Optical Flow](https://mlsanity.com/paper/2505.05089/) (2025)
- [LC-Flow: Learning Local Continuous Optical Flow and Confidence from events](https://mlsanity.com/paper/2605.24604/) (2026)

## Abstract (arXiv)

Event cameras capture changes of illumination in the observed scene rather than accumulating light to create images. Thus, they allow for applications under high-speed motion and complex lighting conditions, where traditional framebased sensors show their limits with blur and over- or underexposed pixels. Thanks to these unique properties, they represent nowadays an highly attractive sensor for ITS-related applications. Event-based optical flow (EBOF) has been studied following the rise in popularity of these neuromorphic cameras. The recent arrival of high-definition neuromorphic sensors, however, challenges the existing approaches, because of the increased resolution of the events pixel array and a much higher throughput. As an answer to these points, we propose an optimized framework for computing optical flow in real-time with both low- and high-resolution event cameras. We formulate a novel dense representation for the sparse events flow, in the form of the "inverse exponential distance surface". It serves as an interim frame, designed for the use of proven, state-of-the-art frame-based optical flow computation methods. We evaluate our approach on both low- and high-resolution driving sequences, and show that it often achieves better results than the current state of the art, while also reaching higher frame rates, 250Hz at 346 x 260 pixels and 77Hz at 1280 x 720 pixels.