# Secrets of Edge-Informed Contrast Maximization for Event-Based Vision

arXiv: https://arxiv.org/abs/2409.14611 · HTML: https://mlsanity.com/paper/2409.14611/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 41 references that are in the map.
- First posted on arXiv 22 Sep 2024.

## Builds on (most cited first, 23 of 23)

- [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)
- [End-to-End Learning of Representations for Asynchronous Event-Based Data](https://mlsanity.com/paper/1904.08245/) (2019)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Efficient and Modular Implicit Differentiation](https://mlsanity.com/paper/2105.15183/) (2021)
- [UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss](https://mlsanity.com/paper/1711.07837/) (2017)
- [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)
- [Secrets of Event-Based Optical Flow](https://mlsanity.com/paper/2207.10022/) (2022)
- [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)
- [Dense Continuous-Time Optical Flow from Events and Frames](https://mlsanity.com/paper/2203.13674/) (2022)
- [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)
- [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)
- [RPEFlow: Multimodal Fusion of RGB-PointCloud-Event for Joint Optical Flow and Scene Flow Estimation](https://mlsanity.com/paper/2309.15082/) (2023)
- [Real-time high speed motion prediction using fast aperture-robust event-driven visual flow](https://mlsanity.com/paper/1811.11135/) (2018)

## Built on by (most cited first, 6 of 6)

- [Simultaneous Motion And Noise Estimation with Event Cameras](https://mlsanity.com/paper/2504.04029/) (2025)
- [EvtSlowTV -- A Large and Diverse Dataset for Event-Based Depth Estimation](https://mlsanity.com/paper/2511.02953/) (2025)
- [FracEvent: Event-Camera Simulation via Fractional-Relaxation Pixel Dynamics](https://mlsanity.com/paper/2606.26636/) (2026)
- [UEOF: A Benchmark Dataset for Underwater Event-Based Optical Flow](https://mlsanity.com/paper/2601.10054/) (2026)
- [Probing Geospatial SSL Representations with Environmental Signals](https://mlsanity.com/paper/2607.05207/) (2026)
- [Bridging Event Streams and DiT: Event-Guided Video Frame Interpolation](https://mlsanity.com/paper/2608.10479/) (2026)

## Abstract (arXiv)

Event cameras capture the motion of intensity gradients (edges) in the image plane in the form of rapid asynchronous events. When accumulated in 2D histograms, these events depict overlays of the edges in motion, consequently obscuring the spatial structure of the generating edges. Contrast maximization (CM) is an optimization framework that can reverse this effect and produce sharp spatial structures that resemble the moving intensity gradients by estimating the motion trajectories of the events. Nonetheless, CM is still an underexplored area of research with avenues for improvement. In this paper, we propose a novel hybrid approach that extends CM from uni-modal (events only) to bi-modal (events and edges). We leverage the underpinning concept that, given a reference time, optimally warped events produce sharp gradients consistent with the moving edge at that time. Specifically, we formalize a correlation-based objective to aid CM and provide key insights into the incorporation of multiscale and multireference techniques. Moreover, our edge-informed CM method yields superior sharpness scores and establishes new state-of-the-art event optical flow benchmarks on the MVSEC, DSEC, and ECD datasets.