# Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation

arXiv: https://arxiv.org/abs/2109.04871 · HTML: https://mlsanity.com/paper/2109.04871/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 33 references that are in the map.
- First posted on arXiv 10 Sep 2021.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [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)
- [Delving Deeper into Convolutional Networks for Learning Video Representations](https://mlsanity.com/paper/1511.06432/) (2015)
- [Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation](https://mlsanity.com/paper/1904.05290/) (2019)
- [Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation](https://mlsanity.com/paper/2003.13045/) (2020)
- [Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks](https://mlsanity.com/paper/2003.06696/) (2020)
- [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)
- [A Fusion Approach for Multi-Frame Optical Flow Estimation](https://mlsanity.com/paper/1810.10066/) (2018)
- [Optical Flow based Visual Potential Field for Autonomous Driving](https://mlsanity.com/paper/2006.00168/) (2020)
- [Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network](https://mlsanity.com/paper/1606.09433/) (2016)
- [Spiking Optical Flow for Event-based Sensors Using IBM's TrueNorth Neurosynaptic System](https://mlsanity.com/paper/1710.09820/) (2017)
- [CoT-AMFlow: Adaptive Modulation Network with Co-Teaching Strategy for Unsupervised Optical Flow Estimation](https://mlsanity.com/paper/2011.02156/) (2020)

## Built on by (most cited first, 30 of 50)

- [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)
- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [Learning Dense and Continuous Optical Flow from an Event Camera](https://mlsanity.com/paper/2211.09078/) (2022)
- [Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow](https://mlsanity.com/paper/2303.05214/) (2023)
- [BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation](https://mlsanity.com/paper/2303.07716/) (2023)
- [TMA: Temporal Motion Aggregation for Event-based Optical Flow](https://mlsanity.com/paper/2303.11629/) (2023)
- [Lightweight Event-based Optical Flow Estimation via Iterative Deblurring](https://mlsanity.com/paper/2211.13726/) (2022)
- [Optical Flow Estimation for Spiking Camera](https://mlsanity.com/paper/2110.03916/) (2021)
- [Learning Optical Flow from Event Camera with Rendered Dataset](https://mlsanity.com/paper/2303.11011/) (2023)
- [Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation](https://mlsanity.com/paper/2407.10802/) (2024)
- [Dual Memory Aggregation Network for Event-Based Object Detection with Learnable Representation](https://mlsanity.com/paper/2303.09919/) (2023)
- [Event-based Temporally Dense Optical Flow Estimation with Sequential Learning](https://mlsanity.com/paper/2210.01244/) (2022)
- [Towards Anytime Optical Flow Estimation with Event Cameras](https://mlsanity.com/paper/2307.05033/) (2023)
- [BlinkVision: A Benchmark for Optical Flow, Scene Flow and Point Tracking Estimation using RGB Frames and Events](https://mlsanity.com/paper/2410.20451/) (2024)
- [Temporal Event Stereo via Joint Learning with Stereoscopic Flow](https://mlsanity.com/paper/2407.10831/) (2024)
- [E-Motion: Future Motion Simulation via Event Sequence Diffusion](https://mlsanity.com/paper/2410.08649/) (2024)
- [Fast Event-based Optical Flow Estimation by Triplet Matching](https://mlsanity.com/paper/2212.12218/) (2022)
- [Globally Optimal Event-Based Divergence Estimation for Ventral Landing](https://mlsanity.com/paper/2209.13168/) (2022)
- [EV-MGRFlowNet: Motion-Guided Recurrent Network for Unsupervised Event-based Optical Flow with Hybrid Motion-Compensation Loss](https://mlsanity.com/paper/2305.07853/) (2023)
- [Event-Free Moving Object Segmentation from Moving Ego Vehicle](https://mlsanity.com/paper/2305.00126/) (2023)
- [EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision](https://mlsanity.com/paper/2412.07080/) (2024)
- [EMatch: A Unified Framework for Event-based Optical Flow and Stereo Matching](https://mlsanity.com/paper/2407.21735/) (2024)
- [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)
- [Unsupervised Optical Flow Estimation with Dynamic Timing Representation for Spike Camera](https://mlsanity.com/paper/2307.06003/) (2023)
- [Dark-EvGS: Event Camera as an Eye for Radiance Field in the Dark](https://mlsanity.com/paper/2507.11931/) (2025)
- [Vector-Symbolic Architecture for Event-Based Optical Flow](https://mlsanity.com/paper/2405.08300/) (2024)
- [MATE: Motion-Augmented Temporal Consistency for Event-based Point Tracking](https://mlsanity.com/paper/2412.01300/) (2024)
- [Spatially-guided Temporal Aggregation for Robust Event-RGB Optical Flow Estimation](https://mlsanity.com/paper/2501.00838/) (2025)

## Abstract (arXiv)

Event camera has offered promising alternative for visual perception, especially in high speed and high dynamic range scenes. Recently, many deep learning methods have shown great success in providing promising solutions to many event-based problems, such as optical flow estimation. However, existing deep learning methods did not address the importance of temporal information well from the perspective of architecture design and cannot effectively extract spatio-temporal features. Another line of research that utilizes Spiking Neural Network suffers from training issues for deeper architecture.To address these points, a novel input representation is proposed that captures the events' temporal distribution for signal enhancement. Moreover, we introduce a spatio-temporal recurrent encoding-decoding neural network architecture for event-based optical flow estimation, which utilizes Convolutional Gated Recurrent Units to extract feature maps from a series of event images. Besides, our architecture allows some traditional frame-based core modules, such as correlation layer and iterative residual refine scheme, to be incorporated. The network is end-to-end trained with self-supervised learning on the Multi-Vehicle Stereo Event Camera dataset. We have shown that it outperforms all the existing state-of-the-art methods by a large margin. The code link is https://github.com/ruizhao26/STE-FlowNet.