# SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap

arXiv: https://arxiv.org/abs/2110.03101 · HTML: https://mlsanity.com/paper/2110.03101/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 52 references that are in the map.
- First posted on arXiv 6 Oct 2021.

## Builds on (most cited first, 23 of 23)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [VisDA: The Visual Domain Adaptation Challenge](https://mlsanity.com/paper/1710.06924/) (2017)
- [HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation](https://mlsanity.com/paper/1908.10357/) (2019)
- [Exploring the structure of a real-time, arbitrary neural artistic stylization network](https://mlsanity.com/paper/1705.06830/) (2017)
- [Style Augmentation: Data Augmentation via Style Randomization](https://mlsanity.com/paper/1809.05375/) (2018)
- [Satellite Pose Estimation Challenge: Dataset, Competition Design and Results](https://mlsanity.com/paper/1911.02050/) (2019)
- [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)
- [DeceptionNet: Network-Driven Domain Randomization](https://mlsanity.com/paper/1904.02750/) (2019)
- [Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks](https://mlsanity.com/paper/1809.07238/) (2018)
- [Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation](https://mlsanity.com/paper/1806.09755/) (2018)
- [Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft](https://mlsanity.com/paper/1909.00392/) (2019)
- [A Spacecraft Dataset for Detection, Segmentation and Parts Recognition](https://mlsanity.com/paper/2106.08186/) (2021)
- [Real-Time, Flight-Ready, Non-Cooperative Spacecraft Pose Estimation Using Monocular Imagery](https://mlsanity.com/paper/2101.09553/) (2021)
- [Robotic Testbed for Rendezvous and Optical Navigation: Multi-Source Calibration and Machine Learning Use Cases](https://mlsanity.com/paper/2108.05529/) (2021)

## Built on by (most cited first, 30 of 57)

- [Robust Multi-Task Learning and Online Refinement for Spacecraft Pose Estimation across Domain Gap](https://mlsanity.com/paper/2203.04275/) (2022)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing](https://mlsanity.com/paper/2209.11945/) (2022)
- [Bridging the Domain Gap in Satellite Pose Estimation: a Self-Training Approach based on Geometrical Constraints](https://mlsanity.com/paper/2212.12103/) (2022)
- [SPADES: A Realistic Spacecraft Pose Estimation Dataset using Event Sensing](https://mlsanity.com/paper/2311.05310/) (2023)
- [Spacecraft Pose Estimation Based on Unsupervised Domain Adaptation and on a 3D-Guided Loss Combination](https://mlsanity.com/paper/2212.13415/) (2022)
- [6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics](https://mlsanity.com/paper/2303.13241/) (2023)
- [Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting](https://mlsanity.com/paper/2401.02588/) (2024)
- [Learning to Predict 3D Rotational Dynamics from Images of a Rigid Body with Unknown Mass Distribution](https://mlsanity.com/paper/2308.14666/) (2023)
- [Leveraging Neural Radiance Fields for Pose Estimation of an Unknown Space Object during Proximity Operations](https://mlsanity.com/paper/2405.12728/) (2024)
- [Bridging the Domain Gap for Flight-Ready Spaceborne Vision](https://mlsanity.com/paper/2409.11661/) (2024)
- [Domain Generalization for 6D Pose Estimation Through NeRF-based Image Synthesis](https://mlsanity.com/paper/2407.10762/) (2024)
- [Uncertainty-Aware Knowledge Distillation for Compact and Efficient 6DoF Pose Estimation](https://mlsanity.com/paper/2503.13053/) (2025)
- [On the Generation of a Synthetic Event-Based Vision Dataset for Navigation and Landing](https://mlsanity.com/paper/2308.00394/) (2023)
- [FastPose-ViT: A Vision Transformer for Real-Time Spacecraft Pose Estimation](https://mlsanity.com/paper/2512.09792/) (2025)
- [Lessons from a Space Lab -- An Image Acquisition Perspective](https://mlsanity.com/paper/2208.08865/) (2022)
- [Event-based Structure-from-Orbit](https://mlsanity.com/paper/2405.06216/) (2024)
- [Domain Generalization for In-Orbit 6D Pose Estimation](https://mlsanity.com/paper/2406.11743/) (2024)
- [Test-Time Certifiable Self-Supervision to Bridge the Sim2Real Gap in Event-Based Satellite Pose Estimation](https://mlsanity.com/paper/2409.06240/) (2024)
- [How Important are Data Augmentations to Close the Domain Gap for Object Detection in Orbit?](https://mlsanity.com/paper/2410.15766/) (2024)
- [SU-Net: Pose estimation network for non-cooperative spacecraft on-orbit](https://mlsanity.com/paper/2302.10602/) (2023)
- [3D-Aware Object Localization using Gaussian Implicit Occupancy Function](https://mlsanity.com/paper/2303.02058/) (2023)
- [Motion Aware ViT-based Framework for Monocular 6-DoF Spacecraft Pose Estimation](https://mlsanity.com/paper/2509.06000/) (2025)
- [NeRF-based Visualization of 3D Cues Supporting Data-Driven Spacecraft Pose Estimation](https://mlsanity.com/paper/2509.14890/) (2025)
- [Improved 3D Gaussian Splatting of Unknown Spacecraft Structure Using Space Environment Illumination Knowledge](https://mlsanity.com/paper/2512.23998/) (2025)
- [SpaceSense-Bench: A Large-Scale Multi-Modal Benchmark for Spacecraft Perception and Pose Estimation](https://mlsanity.com/paper/2603.09320/) (2026)
- [Reconstructing Satellites in 3D from Amateur Telescope Images](https://mlsanity.com/paper/2404.18394/) (2024)
- [Computer vision tasks for intelligent aerospace missions: An overview](https://mlsanity.com/paper/2407.06513/) (2024)
- [Test-Time Adaptation for Keypoint-Based Spacecraft Pose Estimation Based on Predicted-View Synthesis](https://mlsanity.com/paper/2410.04298/) (2024)
- [Joint attitude estimation and 3D neural reconstruction of non-cooperative space objects](https://mlsanity.com/paper/2506.20638/) (2025)

## Abstract (arXiv)

Autonomous vision-based spaceborne navigation is an enabling technology for future on-orbit servicing and space logistics missions. While computer vision in general has benefited from Machine Learning (ML), training and validating spaceborne ML models are extremely challenging due to the impracticality of acquiring a large-scale labeled dataset of images of the intended target in the space environment. Existing datasets, such as Spacecraft PosE Estimation Dataset (SPEED), have so far mostly relied on synthetic images for both training and validation, which are easy to mass-produce but fail to resemble the visual features and illumination variability inherent to the target spaceborne images. In order to bridge the gap between the current practices and the intended applications in future space missions, this paper introduces SPEED+: the next generation spacecraft pose estimation dataset with specific emphasis on domain gap. In addition to 60,000 synthetic images for training, SPEED+ includes 9,531 hardware-in-the-loop images of a spacecraft mockup model captured from the Testbed for Rendezvous and Optical Navigation (TRON) facility. TRON is a first-of-a-kind robotic testbed capable of capturing an arbitrary number of target images with accurate and maximally diverse pose labels and high-fidelity spaceborne illumination conditions. SPEED+ is used in the second international Satellite Pose Estimation Challenge co-hosted by SLAB and the Advanced Concepts Team of the European Space Agency to evaluate and compare the robustness of spaceborne ML models trained on synthetic images.