# Realtime Time Synchronized Event-based Stereo

arXiv: https://arxiv.org/abs/1803.09025 · HTML: https://mlsanity.com/paper/1803.09025/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2018.

## Built on by (most cited first, 28 of 28)

- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [EventCLIP: Adapting CLIP for Event-based Object Recognition](https://mlsanity.com/paper/2306.06354/) (2023)
- [Globally-Optimal Contrast Maximisation for Event Cameras](https://mlsanity.com/paper/2206.05127/) (2022)
- [StereoSpike: Depth Learning with a Spiking Neural Network](https://mlsanity.com/paper/2109.13751/) (2021)
- [Multi-Event-Camera Depth Estimation and Outlier Rejection by Refocused Events Fusion](https://mlsanity.com/paper/2207.10494/) (2022)
- [EVEN: An Event-Based Framework for Monocular Depth Estimation at Adverse Night Conditions](https://mlsanity.com/paper/2302.03860/) (2023)
- [Event-based Stereo Depth Estimation: A Survey](https://mlsanity.com/paper/2409.17680/) (2024)
- [Event-based Continuous Color Video Decompression from Single Frames](https://mlsanity.com/paper/2312.00113/) (2023)
- [Visual Odometry with an Event Camera Using Continuous Ray Warping and Volumetric Contrast Maximization](https://mlsanity.com/paper/2107.03011/) (2021)
- [Dynamic Event Camera Calibration](https://mlsanity.com/paper/2107.06749/) (2021)
- [Temporal Event Stereo via Joint Learning with Stereoscopic Flow](https://mlsanity.com/paper/2407.10831/) (2024)
- [Motion and Structure from Event-based Normal Flow](https://mlsanity.com/paper/2407.12239/) (2024)
- [EvAC3D: From Event-based Apparent Contours to 3D Models via Continuous Visual Hulls](https://mlsanity.com/paper/2304.05296/) (2023)
- [Globally-Optimal Event Camera Motion Estimation](https://mlsanity.com/paper/2203.03914/) (2022)
- [Dense Voxel 3D Reconstruction Using a Monocular Event Camera](https://mlsanity.com/paper/2309.00385/) (2023)
- [ES-PTAM: Event-based Stereo Parallel Tracking and Mapping](https://mlsanity.com/paper/2408.15605/) (2024)
- [EMatch: A Unified Framework for Event-based Optical Flow and Stereo Matching](https://mlsanity.com/paper/2407.21735/) (2024)
- [A Survey of 3D Reconstruction with Event Cameras](https://mlsanity.com/paper/2505.08438/) (2025)
- [DERD-Net: Learning Depth from Event-based Ray Densities](https://mlsanity.com/paper/2504.15863/) (2025)
- [Unleashing the Temporal Potential of Stereo Event Cameras for Continuous-Time 3D Object Detection](https://mlsanity.com/paper/2508.02288/) (2025)
- [URNet: Uncertainty-aware Refinement Network for Event-based Stereo Depth Estimation](https://mlsanity.com/paper/2509.18184/) (2025)
- [MSS-DepthNet: Depth Prediction with Multi-Step Spiking Neural Network](https://mlsanity.com/paper/2211.12156/) (2022)
- [EAG3R: Event-Augmented 3D Geometry Estimation for Dynamic and Extreme-Lighting Scenes](https://mlsanity.com/paper/2512.00771/) (2025)
- [Uncertainty Guided Depth Fusion for Spike Camera](https://mlsanity.com/paper/2208.12653/) (2022)
- [A Survey on Event-driven 3D Reconstruction: Development under Different Categories](https://mlsanity.com/paper/2503.19753/) (2025)
- [Match-Any-Events: Zero-Shot Motion-Robust Feature Matching Across Wide Baselines for Event Cameras](https://mlsanity.com/paper/2604.18744/) (2026)
- [PS-EIP: Robust Photometric Stereo Based on Event Interval Profile](https://mlsanity.com/paper/2503.18341/) (2025)
- [E-TraMamba: A New Paradigm for Efficient Long-Term 3D Feature Tracking with Event Cameras](https://mlsanity.com/paper/2607.02866/) (2026)

## Abstract (arXiv)

In this work, we propose a novel event based stereo method which addresses the problem of motion blur for a moving event camera. Our method uses the velocity of the camera and a range of disparities to synchronize the positions of the events, as if they were captured at a single point in time. We represent these events using a pair of novel time synchronized event disparity volumes, which we show remove motion blur for pixels at the correct disparity in the volume, while further blurring pixels at the wrong disparity. We then apply a novel matching cost over these time synchronized event disparity volumes, which both rewards similarity between the volumes while penalizing blurriness. We show that our method outperforms more expensive, smoothing based event stereo methods, by evaluating on the Multi Vehicle Stereo Event Camera dataset.