# Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing

arXiv: https://arxiv.org/abs/2209.11945 · HTML: https://mlsanity.com/paper/2209.11945/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 53 references that are in the map.
- First posted on arXiv 24 Sep 2022.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](https://mlsanity.com/paper/1803.07913/) (2018)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Learning to Detect Objects with a 1 Megapixel Event Camera](https://mlsanity.com/paper/2009.13436/) (2020)
- [Video to Events: Recycling Video Datasets for Event Cameras](https://mlsanity.com/paper/1912.03095/) (2019)
- [A Survey of Structure from Motion](https://mlsanity.com/paper/1701.08493/) (2017)
- [Domain Adaptation for Visual Applications: A Comprehensive Survey](https://mlsanity.com/paper/1702.05374/) (2017)
- [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018)
- [Satellite Pose Estimation Challenge: Dataset, Competition Design and Results](https://mlsanity.com/paper/1911.02050/) (2019)
- [SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap](https://mlsanity.com/paper/2110.03101/) (2021)
- [Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement](https://mlsanity.com/paper/1908.11542/) (2019)
- [Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering](https://mlsanity.com/paper/1907.04298/) (2019)
- [Wide-Depth-Range 6D Object Pose Estimation in Space](https://mlsanity.com/paper/2104.00337/) (2021)
- [Robust Multi-Task Learning and Online Refinement for Spacecraft Pose Estimation across Domain Gap](https://mlsanity.com/paper/2203.04275/) (2022)
- [Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks](https://mlsanity.com/paper/1809.07238/) (2018)
- [Exploring Event Camera-based Odometry for Planetary Robots](https://mlsanity.com/paper/2204.05880/) (2022)
- [Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview](https://mlsanity.com/paper/2105.14291/) (2021)
- [Star Tracking using an Event Camera](https://mlsanity.com/paper/1812.02895/) (2018)
- [Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation](https://mlsanity.com/paper/1806.09755/) (2018)
- [Efficient Human Pose Estimation via 3D Event Point Cloud](https://mlsanity.com/paper/2206.04511/) (2022)
- [A Spacecraft Dataset for Detection, Segmentation and Parts Recognition](https://mlsanity.com/paper/2106.08186/) (2021)
- [Pose Estimation for Non-Cooperative Rendezvous Using Neural Networks](https://mlsanity.com/paper/1906.09868/) (2019)
- [A Review on Object Pose Recovery: from 3D Bounding Box Detectors to Full 6D Pose Estimators](https://mlsanity.com/paper/2001.10609/) (2020)
- [SPARK: SPAcecraft Recognition leveraging Knowledge of Space Environment](https://mlsanity.com/paper/2104.05978/) (2021)
- [Research on Event Accumulator Settings for Event-Based SLAM](https://mlsanity.com/paper/2112.00427/) (2021)
- [Real-Time 6DOF Pose Relocalization for Event Cameras with Stacked Spatial LSTM Networks](https://mlsanity.com/paper/1708.09011/) (2017)

## Built on by (most cited first, 21 of 21)

- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [ZS6D: Zero-shot 6D Object Pose Estimation using Vision Transformers](https://mlsanity.com/paper/2309.11986/) (2023)
- [A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospects](https://mlsanity.com/paper/2305.07348/) (2023)
- [Line-based 6-DoF Object Pose Estimation and Tracking With an Event Camera](https://mlsanity.com/paper/2408.03225/) (2024)
- [Challenges for Monocular 6D Object Pose Estimation in Robotics](https://mlsanity.com/paper/2307.12172/) (2023)
- [Stereo Event-based, 6-DOF Pose Tracking for Uncooperative Spacecraft](https://mlsanity.com/paper/2503.12732/) (2025)
- [SPADES: A Realistic Spacecraft Pose Estimation Dataset using Event Sensing](https://mlsanity.com/paper/2311.05310/) (2023)
- [Neuromorphic Computing and Sensing in Space](https://mlsanity.com/paper/2212.05236/) (2022)
- [JSTR: Joint Spatio-Temporal Reasoning for Event-based Moving Object Detection](https://mlsanity.com/paper/2403.07436/) (2024)
- [High Frequency, High Accuracy Pointing onboard Nanosats using Neuromorphic Event Sensing and Piezoelectric Actuation](https://mlsanity.com/paper/2309.01361/) (2023)
- [Event-based Structure-from-Orbit](https://mlsanity.com/paper/2405.06216/) (2024)
- [Test-Time Certifiable Self-Supervision to Bridge the Sim2Real Gap in Event-Based Satellite Pose Estimation](https://mlsanity.com/paper/2409.06240/) (2024)
- [Spiking monocular event based 6D pose estimation for space application](https://mlsanity.com/paper/2501.02916/) (2025)
- [Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: a Review](https://mlsanity.com/paper/2504.08588/) (2025)
- [Event-based Star Tracking under Spacecraft Jitter: the e-STURT Dataset](https://mlsanity.com/paper/2505.12588/) (2025)
- [Event-RGB Fusion for Spacecraft Pose Estimation Under Harsh Lighting](https://mlsanity.com/paper/2507.05698/) (2025)
- [Hardware-aware vs. Hardware-agnostic Energy Estimation for SNN in Space Applications](https://mlsanity.com/paper/2508.19654/) (2025)
- [Mixing Data-driven and Geometric Models for Satellite Docking Port State Estimation using an RGB or Event Camera](https://mlsanity.com/paper/2409.15581/) (2024)
- [A large-scale, physically-based synthetic dataset for satellite pose estimation](https://mlsanity.com/paper/2506.12782/) (2025)
- [CAD-Free Learning of Spacecraft Pose Estimators via NeRF-Based Augmentations](https://mlsanity.com/paper/2605.19649/) (2026)
- [Robust PnP on a Neuromorphic Processor for Object Pose Estimation](https://mlsanity.com/paper/2607.16834/) (2026)

## Abstract (arXiv)

Deep models trained using synthetic data require domain adaptation to bridge the gap between the simulation and target environments. State-of-the-art domain adaptation methods often demand sufficient amounts of (unlabelled) data from the target domain. However, this need is difficult to fulfil when the target domain is an extreme environment, such as space. In this paper, our target problem is close proximity satellite pose estimation, where it is costly to obtain images of satellites from actual rendezvous missions. We demonstrate that event sensing offers a promising solution to generalise from the simulation to the target domain under stark illumination differences. Our main contribution is an event-based satellite pose estimation technique, trained purely on synthetic event data with basic data augmentation to improve robustness against practical (noisy) event sensors. Underpinning our method is a novel dataset with carefully calibrated ground truth, comprising of real event data obtained by emulating satellite rendezvous scenarios in the lab under drastic lighting conditions. Results on the dataset showed that our event-based satellite pose estimation method, trained only on synthetic data without adaptation, could generalise to the target domain effectively.