# ResFlow: Fine-tuning Residual Optical Flow for Event-based High Temporal Resolution Motion Estimation

arXiv: https://arxiv.org/abs/2412.09105 · HTML: https://mlsanity.com/paper/2412.09105/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 62 references that are in the map.
- First posted on arXiv 12 Dec 2024.

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

- [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)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [GMFlow: Learning Optical Flow via Global Matching](https://mlsanity.com/paper/2111.13680/) (2021)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [FlowFormer: A Transformer Architecture for Optical Flow](https://mlsanity.com/paper/2203.16194/) (2022)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [Learning to Estimate Hidden Motions with Global Motion Aggregation](https://mlsanity.com/paper/2104.02409/) (2021)
- [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)
- [VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows](https://mlsanity.com/paper/2108.05015/) (2021)
- [The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation](https://mlsanity.com/paper/2306.01923/) (2023)
- [Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks](https://mlsanity.com/paper/2106.01862/) (2021)
- [Secrets of Event-Based Optical Flow](https://mlsanity.com/paper/2207.10022/) (2022)
- [Learning Dense and Continuous Optical Flow from an Event Camera](https://mlsanity.com/paper/2211.09078/) (2022)
- [Dense Continuous-Time Optical Flow from Events and Frames](https://mlsanity.com/paper/2203.13674/) (2022)
- [Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackers](https://mlsanity.com/paper/2307.04129/) (2023)
- [Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow](https://mlsanity.com/paper/2303.05214/) (2023)
- [Event Collapse in Contrast Maximization Frameworks](https://mlsanity.com/paper/2207.04007/) (2022)
- [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)
- [Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation](https://mlsanity.com/paper/2407.10802/) (2024)
- [Event Camera Data Dense Pre-training](https://mlsanity.com/paper/2311.11533/) (2023)
- [Event Voxel Set Transformer for Spatiotemporal Representation Learning on Event Streams](https://mlsanity.com/paper/2303.03856/) (2023)
- [CRSOT: Cross-Resolution Object Tracking using Unaligned Frame and Event Cameras](https://mlsanity.com/paper/2401.02826/) (2024)
- [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)
- [E-Motion: Future Motion Simulation via Event Sequence Diffusion](https://mlsanity.com/paper/2410.08649/) (2024)
- [Modeling State Shifting via Local-Global Distillation for Event-Frame Gaze Tracking](https://mlsanity.com/paper/2404.00548/) (2024)

## Built on by (most cited first, 3 of 3)

- [Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application](https://mlsanity.com/paper/2503.22943/) (2025)
- [From Contrast to Consistency: Rethinking Event-based Continuous-Time Optical Flow Estimation](https://mlsanity.com/paper/2605.25570/) (2026)
- [E-WAVE: Event-based Continuous Optical Flow via Warping-Aligned Visual Encoding](https://mlsanity.com/paper/2609.34346/) (2026)

## Abstract (arXiv)

Event cameras hold significant promise for high-temporal-resolution (HTR) motion estimation. However, estimating event-based HTR optical flow faces two key challenges: the absence of HTR ground-truth data and the intrinsic sparsity of event data. Most existing approaches rely on the flow accumulation paradigms to indirectly supervise intermediate flows, often resulting in accumulation errors and optimization difficulties. To address these challenges, we propose a residual-based paradigm for estimating HTR optical flow with event data. Our approach separates HTR flow estimation into two stages: global linear motion estimation and HTR residual flow refinement. The residual paradigm effectively mitigates the impacts of event sparsity on optimization and is compatible with any LTR algorithm. Next, to address the challenge posed by the absence of HTR ground truth, we incorporate novel learning strategies. Specifically, we initially employ a shared refiner to estimate the residual flows, enabling both LTR supervision and HTR inference. Subsequently, we introduce regional noise to simulate the residual patterns of intermediate flows, facilitating the adaptation from LTR supervision to HTR inference. Additionally, we show that the noise-based strategy supports in-domain self-supervised training. Comprehensive experimental results demonstrate that our approach achieves state-of-the-art accuracy in both LTR and HTR metrics, highlighting its effectiveness and superiority.