# A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospects

arXiv: https://arxiv.org/abs/2305.07348 · HTML: https://mlsanity.com/paper/2305.07348/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 93 of its 194 references that are in the map.
- First posted on arXiv 12 May 2023.

## Builds on (most cited first, 30 of 93)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [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)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)

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

- [Stereo Event-based, 6-DOF Pose Tracking for Uncooperative Spacecraft](https://mlsanity.com/paper/2503.12732/) (2025)
- [Leveraging Neural Radiance Fields for Pose Estimation of an Unknown Space Object during Proximity Operations](https://mlsanity.com/paper/2405.12728/) (2024)
- [FastPose-ViT: A Vision Transformer for Real-Time Spacecraft Pose Estimation](https://mlsanity.com/paper/2512.09792/) (2025)
- [Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiation](https://mlsanity.com/paper/2405.02642/) (2024)
- [Vision-Based Detection of Uncooperative Targets and Components on Small Satellites](https://mlsanity.com/paper/2408.12084/) (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)
- [NeRF-based Visualization of 3D Cues Supporting Data-Driven Spacecraft Pose Estimation](https://mlsanity.com/paper/2509.14890/) (2025)
- [Markers Identification for Relative Pose Estimation of an Uncooperative Target](https://mlsanity.com/paper/2407.20515/) (2024)
- [Event-RGB Fusion for Spacecraft Pose Estimation Under Harsh Lighting](https://mlsanity.com/paper/2507.05698/) (2025)
- [FPG-NAS: FLOPs-Aware Gated Differentiable Neural Architecture Search for Efficient 6DoF Pose Estimation](https://mlsanity.com/paper/2508.03618/) (2025)
- [Hardware-aware vs. Hardware-agnostic Energy Estimation for SNN in Space Applications](https://mlsanity.com/paper/2508.19654/) (2025)
- [Cov2Pose: Leveraging Spatial Covariance for Direct Manifold-aware 6-DoF Object Pose Estimation](https://mlsanity.com/paper/2603.19961/) (2026)
- [Efficient Onboard Spacecraft Pose Estimation with Event Cameras and Neuromorphic Hardware](https://mlsanity.com/paper/2604.04117/) (2026)
- [Component-Aware Structure-Preserving Style Transfer for Satellite Visual Sim2Real Data Construction](https://mlsanity.com/paper/2605.19624/) (2026)
- [GABI: Geometry-Aware Boundary Integration for Spacecraft Segmentation](https://mlsanity.com/paper/2606.00886/) (2026)
- [Precision-Aware Illumination-Disentangled Vision Transformer for Spacecraft 6D Pose Estimation](https://mlsanity.com/paper/2606.11619/) (2026)
- [Segmentation-based Detection for Efficient Multi-Task Spacecraft Perception](https://mlsanity.com/paper/2606.15409/) (2026)
- [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)
- [GKNet: Graph-based Keypoints Network for Monocular Pose Estimation of Non-cooperative Spacecraft](https://mlsanity.com/paper/2507.11077/) (2025)
- [Adaptive Relative Pose Estimation Framework with Dual Noise Tuning for Safe Approaching Maneuvers](https://mlsanity.com/paper/2507.16214/) (2025)
- [Bridging the Synthetic-Real Gap: Supervised Domain Adaptation for Robust Spacecraft 6-DoF Pose Estimation](https://mlsanity.com/paper/2509.13792/) (2025)
- [Navigation Around Unknown Space Objects Using Visible-Thermal Image Fusion](https://mlsanity.com/paper/2512.12203/) (2025)
- [SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing](https://mlsanity.com/paper/2604.14399/) (2026)
- [TALON: Token-Aligned Lightweight Adapters for 6-DoF Spacecraft Pose Estimation](https://mlsanity.com/paper/2605.31217/) (2026)
- [GAP-GDRNet: Geometry-aware monocular 6D pose estimation for spacecraft using synthetic geometric supervision](https://mlsanity.com/paper/2607.02360/) (2026)
- [DreamSat-Pose: Spacecraft Pose Estimation from Single-View 3D Reconstructions and Learned 2D-3D Feature Matching](https://mlsanity.com/paper/2607.13449/) (2026)
- [GeoDistill-Refine: Silhouette-First Geometry Distillation for Annotation-Free Spacecraft Segmentation](https://mlsanity.com/paper/2608.07405/) (2026)
- [HAT: Hypothesis-Anchored Tracking for Video Monocular Spacecraft Pose Estimation](https://mlsanity.com/paper/2609.21597/) (2026)

## Abstract (arXiv)

Estimating the pose of an uncooperative spacecraft is an important computer vision problem for enabling the deployment of automatic vision-based systems in orbit, with applications ranging from on-orbit servicing to space debris removal. Following the general trend in computer vision, more and more works have been focusing on leveraging Deep Learning (DL) methods to address this problem. However and despite promising research-stage results, major challenges preventing the use of such methods in real-life missions still stand in the way. In particular, the deployment of such computation-intensive algorithms is still under-investigated, while the performance drop when training on synthetic and testing on real images remains to mitigate. The primary goal of this survey is to describe the current DL-based methods for spacecraft pose estimation in a comprehensive manner. The secondary goal is to help define the limitations towards the effective deployment of DL-based spacecraft pose estimation solutions for reliable autonomous vision-based applications. To this end, the survey first summarises the existing algorithms according to two approaches: hybrid modular pipelines and direct end-to-end regression methods. A comparison of algorithms is presented not only in terms of pose accuracy but also with a focus on network architectures and models' sizes keeping potential deployment in mind. Then, current monocular spacecraft pose estimation datasets used to train and test these methods are discussed. The data generation methods: simulators and testbeds, the domain gap and the performance drop between synthetically generated and lab/space collected images and the potential solutions are also discussed. Finally, the paper presents open research questions and future directions in the field, drawing parallels with other computer vision applications.