# SPADES: A Realistic Spacecraft Pose Estimation Dataset using Event Sensing

arXiv: https://arxiv.org/abs/2311.05310 · HTML: https://mlsanity.com/paper/2311.05310/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization](https://mlsanity.com/paper/1909.06043/) (2019)
- [EventNet: Asynchronous Recursive Event Processing](https://mlsanity.com/paper/1812.07045/) (2018)
- [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)
- [How to Calibrate Your Event Camera](https://mlsanity.com/paper/2105.12362/) (2021)
- [EventHands: Real-Time Neural 3D Hand Pose Estimation from an Event Stream](https://mlsanity.com/paper/2012.06475/) (2020)
- [UnrealCV: Connecting Computer Vision to Unreal Engine](https://mlsanity.com/paper/1609.01326/) (2016)
- [Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks](https://mlsanity.com/paper/1809.07238/) (2018)
- [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)
- [Bayesian Attitude Estimation with the Matrix Fisher Distribution on SO(3)](https://mlsanity.com/paper/1710.03746/) (2017)
- [Lessons from a Space Lab -- An Image Acquisition Perspective](https://mlsanity.com/paper/2208.08865/) (2022)

## Built on by (most cited first, 13 of 13)

- [Stereo Event-based, 6-DOF Pose Tracking for Uncooperative Spacecraft](https://mlsanity.com/paper/2503.12732/) (2025)
- [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)
- [Motion Aware ViT-based Framework for Monocular 6-DoF Spacecraft Pose Estimation](https://mlsanity.com/paper/2509.06000/) (2025)
- [VAPO: Visibility-Aware Keypoint Localization for Efficient 6DoF Object Pose Estimation](https://mlsanity.com/paper/2403.14559/) (2024)
- [Event-RGB Fusion for Spacecraft Pose Estimation Under Harsh Lighting](https://mlsanity.com/paper/2507.05698/) (2025)
- [Efficient Onboard Spacecraft Pose Estimation with Event Cameras and Neuromorphic Hardware](https://mlsanity.com/paper/2604.04117/) (2026)
- [A large-scale, physically-based synthetic dataset for satellite pose estimation](https://mlsanity.com/paper/2506.12782/) (2025)
- [Bridging the Synthetic-Real Gap: Supervised Domain Adaptation for Robust Spacecraft 6-DoF Pose Estimation](https://mlsanity.com/paper/2509.13792/) (2025)
- [Keypoint-based Dynamic Object 6-DoF Pose Tracking via Event Camera](https://mlsanity.com/paper/2604.23387/) (2026)
- [TALON: Token-Aligned Lightweight Adapters for 6-DoF Spacecraft Pose Estimation](https://mlsanity.com/paper/2605.31217/) (2026)
- [HAT: Hypothesis-Anchored Tracking for Video Monocular Spacecraft Pose Estimation](https://mlsanity.com/paper/2609.21597/) (2026)

## Abstract (arXiv)

In recent years, there has been a growing demand for improved autonomy for in-orbit operations such as rendezvous, docking, and proximity maneuvers, leading to increased interest in employing Deep Learning-based Spacecraft Pose Estimation techniques. However, due to limited access to real target datasets, algorithms are often trained using synthetic data and applied in the real domain, resulting in a performance drop due to the domain gap. State-of-the-art approaches employ Domain Adaptation techniques to mitigate this issue. In the search for viable solutions, event sensing has been explored in the past and shown to reduce the domain gap between simulations and real-world scenarios. Event sensors have made significant advancements in hardware and software in recent years. Moreover, the characteristics of the event sensor offer several advantages in space applications compared to RGB sensors. To facilitate further training and evaluation of DL-based models, we introduce a novel dataset, SPADES, comprising real event data acquired in a controlled laboratory environment and simulated event data using the same camera intrinsics. Furthermore, we propose an effective data filtering method to improve the quality of training data, thus enhancing model performance. Additionally, we introduce an image-based event representation that outperforms existing representations. A multifaceted baseline evaluation was conducted using different event representations, event filtering strategies, and algorithmic frameworks, and the results are summarized. The dataset will be made available at http://cvi2.uni.lu/spades.