# Event Cameras versus Frame Cameras for Synthetic-Aperture Imaging: A Simulation Study

arXiv: https://arxiv.org/abs/2610.20309 · HTML: https://mlsanity.com/paper/2610.20309/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 14 Oct 2026.

## Builds on (most cited first, 13 of 13)

- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Video to Events: Recycling Video Datasets for Event Cameras](https://mlsanity.com/paper/1912.03095/) (2019)
- [Seeing Motion at Nighttime with an Event Camera](https://mlsanity.com/paper/2404.11884/) (2024)
- [An Autonomous Drone for Search and Rescue in Forests using Airborne Optical Sectioning](https://mlsanity.com/paper/2105.04328/) (2021)
- [Shining light on the DVS pixel: A tutorial and discussion about biasing and optimization](https://mlsanity.com/paper/2304.04706/) (2023)
- [Event-based Synthetic Aperture Imaging with a Hybrid Network](https://mlsanity.com/paper/2103.02376/) (2021)
- [A Statistical View on Synthetic Aperture Imaging for Occlusion Removal](https://mlsanity.com/paper/1906.06600/) (2019)
- [Unraveling the paradox of intensity-dependent DVS pixel noise](https://mlsanity.com/paper/2109.08640/) (2021)
- [Autonomous Quadrotor Flight despite Rotor Failure with Onboard Vision Sensors: Frames vs. Events](https://mlsanity.com/paper/2102.13406/) (2021)
- [Learning to See Through with Events](https://mlsanity.com/paper/2212.02219/) (2022)
- [Re-Interpreting the Step-Response Probability Curve to Extract Fundamental Physical Parameters of Event-based Vision Sensors](https://mlsanity.com/paper/2404.07656/) (2024)
- [Event-based SLAM Benchmark for High-Speed Maneuvers](https://mlsanity.com/paper/2604.24033/) (2026)

## Abstract (arXiv)

Synthetic-aperture imaging (SAI) removes occluders such as foliage by integrating many views of a scene along a camera path. It has so far been used mainly with frame cameras. We ask whether an event camera has advantages for SAI, and answer the question in simulation, where both sensors see exactly the same scene and the same camera movement. A densely rendered Blender sequence is turned into frames with an EMVA~1288 noise model of an industrial global-shutter camera, and into an event stream with the v2e simulator parameterized like a Sony IMX636. The frames are integrated with the existing airborne optical sectioning renderer. For the events we add a new integration path that projects every single event onto the focal plane at its own interpolated pose, so no events have to be binned into frames. We compare the two sensors in three main runs and an additional dark high-speed test. In a bright scene at 25,m/s the frame camera records only three images on the path and cannot remove the occluder, while the event camera reconstructs the target, even after an event-rate controller drops most of its events to fit the camera link. At the same speed in a 5,lux scene the event camera no longer resolves the target either. In a 5,lux scene at 5,m/s both sensors reconstruct the target at similar quality. We also compare the data one traversal of the aperture costs. At high speed the capped event stream still shows the target at the same data volume as the three frames. At 1,m/s we compare both cameras at two matched data budgets, and there the event camera has no advantage over the frame camera in reconstruction quality. The data rate of the event camera depends on the scene and is highest exactly in the fast bright scenes where it is needed most.