# EV-MGRFlowNet: Motion-Guided Recurrent Network for Unsupervised Event-based Optical Flow with Hybrid Motion-Compensation Loss

arXiv: https://arxiv.org/abs/2305.07853 · HTML: https://mlsanity.com/paper/2305.07853/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 57 references that are in the map.
- First posted on arXiv 13 May 2023.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [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)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars](https://mlsanity.com/paper/1804.01310/) (2018)
- [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)
- [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)
- [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)
- [Secrets of Event-Based Optical Flow](https://mlsanity.com/paper/2207.10022/) (2022)
- [Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction](https://mlsanity.com/paper/2102.09320/) (2021)
- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [Learning Monocular Dense Depth from Events](https://mlsanity.com/paper/2010.08350/) (2020)
- [Focus Is All You Need: Loss Functions For Event-based Vision](https://mlsanity.com/paper/1904.07235/) (2019)
- [Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation](https://mlsanity.com/paper/2109.04871/) (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)
- [Event Collapse in Contrast Maximization Frameworks](https://mlsanity.com/paper/2207.04007/) (2022)
- [Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow](https://mlsanity.com/paper/2112.06242/) (2021)
- [Fusion-FlowNet: Energy-Efficient Optical Flow Estimation using Sensor Fusion and Deep Fused Spiking-Analog Network Architectures](https://mlsanity.com/paper/2103.10592/) (2021)
- [Motion Robust High-Speed Light-Weighted Object Detection With Event Camera](https://mlsanity.com/paper/2208.11602/) (2022)
- [FE-Fusion-VPR: Attention-based Multi-Scale Network Architecture for Visual Place Recognition by Fusing Frames and Events](https://mlsanity.com/paper/2211.12244/) (2022)
- [A Neuromorphic Vision-Based Measurement for Robust Relative Localization in Future Space Exploration Missions](https://mlsanity.com/paper/2206.11541/) (2022)
- [Neuro-Planner: A 3D Visual Navigation Method for MAV with Depth Camera based on Neuromorphic Reinforcement Learning](https://mlsanity.com/paper/2210.02305/) (2022)

## Built on by (most cited first, 9 of 9)

- [EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation](https://mlsanity.com/paper/2506.03512/) (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)
- [Fast Feature Field (F3): A Predictive Representation of Events](https://mlsanity.com/paper/2509.25146/) (2025)
- [E-MoFlow: Learning Egomotion and Optical Flow from Event Data via Implicit Regularization](https://mlsanity.com/paper/2510.12753/) (2025)
- [An Event-Oriented Diffusion-Refinement Method for Sparse Events Completion](https://mlsanity.com/paper/2401.03153/) (2024)
- [EV-MGDispNet: Motion-Guided Event-Based Stereo Disparity Estimation Network with Left-Right Consistency](https://mlsanity.com/paper/2408.05452/) (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 offer promising properties, such as high temporal resolution and high dynamic range. These benefits have been utilized into many machine vision tasks, especially optical flow estimation. Currently, most existing event-based works use deep learning to estimate optical flow. However, their networks have not fully exploited prior hidden states and motion flows. Additionally, their supervision strategy has not fully leveraged the geometric constraints of event data to unlock the potential of networks. In this paper, we propose EV-MGRFlowNet, an unsupervised event-based optical flow estimation pipeline with motion-guided recurrent networks using a hybrid motion-compensation loss. First, we propose a feature-enhanced recurrent encoder network (FERE-Net) which fully utilizes prior hidden states to obtain multi-level motion features. Then, we propose a flow-guided decoder network (FGD-Net) to integrate prior motion flows. Finally, we design a hybrid motion-compensation loss (HMC-Loss) to strengthen geometric constraints for the more accurate alignment of events. Experimental results show that our method outperforms the current state-of-the-art (SOTA) method on the MVSEC dataset, with an average reduction of approximately 22.71% in average endpoint error (AEE). To our knowledge, our method ranks first among unsupervised learning-based methods.