# LC-Flow: Learning Local Continuous Optical Flow and Confidence from events

arXiv: https://arxiv.org/abs/2605.24604 · HTML: https://mlsanity.com/paper/2605.24604/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 41 references that are in the map.
- First posted on arXiv 23 May 2026.

## Builds on (most cited first, 29 of 29)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion](https://mlsanity.com/paper/1812.08156/) (2018)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [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)
- [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)
- [AEGNN: Asynchronous Event-based Graph Neural Networks](https://mlsanity.com/paper/2203.17149/) (2022)
- [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)
- [Time-Ordered Recent Event (TORE) Volumes for Event Cameras](https://mlsanity.com/paper/2103.06108/) (2021)
- [Learning Dense and Continuous Optical Flow from an Event Camera](https://mlsanity.com/paper/2211.09078/) (2022)
- [EventNet: Asynchronous Recursive Event Processing](https://mlsanity.com/paper/1812.07045/) (2018)
- [Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation](https://mlsanity.com/paper/2109.04871/) (2021)
- [Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow](https://mlsanity.com/paper/2303.05214/) (2023)
- [Optical flow estimation from event-based cameras and spiking neural networks](https://mlsanity.com/paper/2302.06492/) (2023)
- [TMA: Temporal Motion Aggregation for Event-based Optical Flow](https://mlsanity.com/paper/2303.11629/) (2023)
- [Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models](https://mlsanity.com/paper/2404.18508/) (2024)
- [Distance Surface for Event-Based Optical Flow](https://mlsanity.com/paper/2003.12680/) (2020)
- [Real-Time Optical Flow for Vehicular Perception with Low- and High-Resolution Event Cameras](https://mlsanity.com/paper/2112.10591/) (2021)
- [Real-time high speed motion prediction using fast aperture-robust event-driven visual flow](https://mlsanity.com/paper/1811.11135/) (2018)
- [Fast Event-based Optical Flow Estimation by Triplet Matching](https://mlsanity.com/paper/2212.12218/) (2022)
- [FlexEvent: Towards Flexible Event-Frame Object Detection at Varying Operational Frequencies](https://mlsanity.com/paper/2412.06708/) (2024)
- [EV-MGRFlowNet: Motion-Guided Recurrent Network for Unsupervised Event-based Optical Flow with Hybrid Motion-Compensation Loss](https://mlsanity.com/paper/2305.07853/) (2023)
- [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)
- [Continuous-Time Human Motion Field from Events](https://mlsanity.com/paper/2412.01747/) (2024)
- [SMF-VO: Direct Ego-Motion Estimation via Sparse Motion Fields](https://mlsanity.com/paper/2511.09072/) (2025)

## Abstract (arXiv)

Event cameras capture brightness changes asynchronously with microsecond resolution, yet existing optical flow methods fail to fully exploit this temporal continuity. Frame-based approaches impose artificial accumulation latency and suffer from domain overfitting, while model-based local methods operate statelessly, discarding temporal history between predictions and yielding inaccurate flows. We propose LC-Flow, the first temporally continuous, learning-based optical flow estimator that operates purely from local events. At its core, a Continuous Local Recurrent Network maintains persistent hidden states per spatial grid, incrementally accumulating temporal context as events arrive. Unlike frame-based methods constrained to fixed accumulation windows, and unlike stateless model-based methods that recompute motion from scratch at each step, LC-Flow produces sparse local flow estimates at arbitrary timestamps with full motion history. To address the inherent ambiguity of local observations, we jointly learn a confidence score that quantifies the reliability of each prediction, explicitly handling event sparsity and the aperture problem. This confidence serves a dual role: filtering unreliable estimates for downstream tasks such as visual odometry, and providing principled weights for a multi-scale confidence-guided aggregation that reconstructs globally consistent flow from the sparse local outputs. LC-Flow achieves state-of-the-art performance among local methods on both MVSEC and DSEC, while the confidence-guided aggregation establishes a new overall state-of-the-art on the MVSEC benchmark, surpassing heavy frame-based networks that rely on global spatial priors.