# Event-based Structure-from-Orbit

arXiv: https://arxiv.org/abs/2405.06216 · HTML: https://mlsanity.com/paper/2405.06216/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 49 references that are in the map.
- First posted on arXiv 10 May 2024.

## Builds on (most cited first, 22 of 22)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion](https://mlsanity.com/paper/1812.08156/) (2018)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [A Survey of Structure from Motion](https://mlsanity.com/paper/1701.08493/) (2017)
- [Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios](https://mlsanity.com/paper/1709.06310/) (2017)
- [Asynchronous, Photometric Feature Tracking using Events and Frames](https://mlsanity.com/paper/1807.09713/) (2018)
- [Event-based Stereo Visual Odometry](https://mlsanity.com/paper/2007.15548/) (2020)
- [Event-aided Direct Sparse Odometry](https://mlsanity.com/paper/2204.07640/) (2022)
- [SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap](https://mlsanity.com/paper/2110.03101/) (2021)
- [Single Image Optical Flow Estimation with an Event Camera](https://mlsanity.com/paper/2004.00347/) (2020)
- [Star Tracking using an Event Camera](https://mlsanity.com/paper/1812.02895/) (2018)
- [FA-Harris: A Fast and Asynchronous Corner Detector for Event Cameras](https://mlsanity.com/paper/1906.10925/) (2019)
- [Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing](https://mlsanity.com/paper/2209.11945/) (2022)
- [Event-based Object Detection and Tracking for Space Situational Awareness](https://mlsanity.com/paper/1911.08730/) (2019)
- [eCDT: Event Clustering for Simultaneous Feature Detection and Tracking-](https://mlsanity.com/paper/2207.09108/) (2022)
- [Event-based Simultaneous Localization and Mapping: A Comprehensive Survey](https://mlsanity.com/paper/2304.09793/) (2023)
- [SPADES: A Realistic Spacecraft Pose Estimation Dataset using Event Sensing](https://mlsanity.com/paper/2311.05310/) (2023)
- [Dense Voxel 3D Reconstruction Using a Monocular Event Camera](https://mlsanity.com/paper/2309.00385/) (2023)
- [Autonomous Guidance Navigation and Control of the VISORS Formation-Flying Mission](https://mlsanity.com/paper/2309.16698/) (2023)
- [Autonomous Asteroid Characterization Through Nanosatellite Swarming](https://mlsanity.com/paper/2210.05518/) (2022)
- [Accidental Turntables: Learning 3D Pose by Watching Objects Turn](https://mlsanity.com/paper/2212.06300/) (2022)

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

- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [A Survey of 3D Reconstruction with Event Cameras](https://mlsanity.com/paper/2505.08438/) (2025)
- [A Survey on Event-driven 3D Reconstruction: Development under Different Categories](https://mlsanity.com/paper/2503.19753/) (2025)

## Abstract (arXiv)

Event sensors offer high temporal resolution visual sensing, which makes them ideal for perceiving fast visual phenomena without suffering from motion blur. Certain applications in robotics and vision-based navigation require 3D perception of an object undergoing circular or spinning motion in front of a static camera, such as recovering the angular velocity and shape of the object. The setting is equivalent to observing a static object with an orbiting camera. In this paper, we propose event-based structure-from-orbit (eSfO), where the aim is to simultaneously reconstruct the 3D structure of a fast spinning object observed from a static event camera, and recover the equivalent orbital motion of the camera. Our contributions are threefold: since state-of-the-art event feature trackers cannot handle periodic self-occlusion due to the spinning motion, we develop a novel event feature tracker based on spatio-temporal clustering and data association that can better track the helical trajectories of valid features in the event data. The feature tracks are then fed to our novel factor graph-based structure-from-orbit back-end that calculates the orbital motion parameters (e.g., spin rate, relative rotational axis) that minimize the reprojection error. For evaluation, we produce a new event dataset of objects under spinning motion. Comparisons against ground truth indicate the efficacy of eSfO.