# Real-Time, Flight-Ready, Non-Cooperative Spacecraft Pose Estimation Using Monocular Imagery

arXiv: https://arxiv.org/abs/2101.09553 · HTML: https://mlsanity.com/paper/2101.09553/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 23 Jan 2021.

## Builds on (most cited first, 13 of 13)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again](https://mlsanity.com/paper/1711.10006/) (2017)
- [Speed/accuracy trade-offs for modern convolutional object detectors](https://mlsanity.com/paper/1611.10012/) (2016)
- [Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views](https://mlsanity.com/paper/1505.05641/) (2015)
- [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)
- [Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks](https://mlsanity.com/paper/1809.07238/) (2018)
- [Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft](https://mlsanity.com/paper/1909.00392/) (2019)
- [MobilePose: Real-Time Pose Estimation for Unseen Objects with Weak Shape Supervision](https://mlsanity.com/paper/2003.03522/) (2020)
- [CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation](https://mlsanity.com/paper/1909.13476/) (2019)
- [DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization](https://mlsanity.com/paper/2003.05523/) (2020)

## Built on by (most cited first, 15 of 15)

- [SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap](https://mlsanity.com/paper/2110.03101/) (2021)
- [Robust Multi-Task Learning and Online Refinement for Spacecraft Pose Estimation across Domain Gap](https://mlsanity.com/paper/2203.04275/) (2022)
- [A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospects](https://mlsanity.com/paper/2305.07348/) (2023)
- [Vision-based Neural Scene Representations for Spacecraft](https://mlsanity.com/paper/2105.06405/) (2021)
- [LSPnet: A 2D Localization-oriented Spacecraft Pose Estimation Neural Network](https://mlsanity.com/paper/2104.09248/) (2021)
- [6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics](https://mlsanity.com/paper/2303.13241/) (2023)
- [Mobile-URSONet: an Embeddable Neural Network for Onboard Spacecraft Pose Estimation](https://mlsanity.com/paper/2205.02065/) (2022)
- [Deep Learning for Real Time Satellite Pose Estimation on Low Power Edge TPU](https://mlsanity.com/paper/2204.03296/) (2022)
- [Domain Generalization for In-Orbit 6D Pose Estimation](https://mlsanity.com/paper/2406.11743/) (2024)
- [Spiking monocular event based 6D pose estimation for space application](https://mlsanity.com/paper/2501.02916/) (2025)
- [Real-Time Spacecraft Pose Estimation Using Mixed-Precision Quantized Neural Network on COTS Reconfigurable MPSoC](https://mlsanity.com/paper/2407.06170/) (2024)
- [Motion Aware ViT-based Framework for Monocular 6-DoF Spacecraft Pose Estimation](https://mlsanity.com/paper/2509.06000/) (2025)
- [SpY: A Context-Based Approach to Spacecraft Component Detection](https://mlsanity.com/paper/2406.18709/) (2024)
- [Deep Learning-Based Image Recovery and Pose Estimation for Resident Space Objects](https://mlsanity.com/paper/2501.13009/) (2025)
- [Enabling Robust, Real-Time Verification of Vision-Based Navigation through View Synthesis](https://mlsanity.com/paper/2507.02993/) (2025)

## Abstract (arXiv)

A key requirement for autonomous on-orbit proximity operations is the estimation of a target spacecraft's relative pose (position and orientation). It is desirable to employ monocular cameras for this problem due to their low cost, weight, and power requirements. This work presents a novel convolutional neural network (CNN)-based monocular pose estimation system that achieves state-of-the-art accuracy with low computational demand. In combination with a Blender-based synthetic data generation scheme, the system demonstrates the ability to generalize from purely synthetic training data to real in-space imagery of the Northrop Grumman Enhanced Cygnus spacecraft. Additionally, the system achieves real-time performance on low-power flight-like hardware.