# AstroVision: Towards Autonomous Feature Detection and Description for Missions to Small Bodies Using Deep Learning

arXiv: https://arxiv.org/abs/2208.02053 · HTML: https://mlsanity.com/paper/2208.02053/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 89 references that are in the map.
- First posted on arXiv 3 Aug 2022.

## Builds on (most cited first, 16 of 16)

- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks](https://mlsanity.com/paper/1911.10127/) (2019)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [D2-Net: A Trainable CNN for Joint Detection and Description of Local Features](https://mlsanity.com/paper/1905.03561/) (2019)
- [LIFT: Learned Invariant Feature Transform](https://mlsanity.com/paper/1603.09114/) (2016)
- [LF-Net: Learning Local Features from Images](https://mlsanity.com/paper/1805.09662/) (2018)
- [ASLFeat: Learning Local Features of Accurate Shape and Localization](https://mlsanity.com/paper/2003.10071/) (2020)
- [TILDE: A Temporally Invariant Learned DEtector](https://mlsanity.com/paper/1411.4568/) (2014)
- [Local Descriptors Optimized for Average Precision](https://mlsanity.com/paper/1804.05312/) (2018)
- [The Final Frontier: Deep Learning in Space](https://mlsanity.com/paper/2001.10362/) (2020)
- [Matchable Image Retrieval by Learning from Surface Reconstruction](https://mlsanity.com/paper/1811.10343/) (2018)
- [Deep Learning-based Spacecraft Relative Navigation Methods: A Survey](https://mlsanity.com/paper/2108.08876/) (2021)
- [Learning to Assign Orientations to Feature Points](https://mlsanity.com/paper/1511.04273/) (2015)
- [GPU4S: Embedded GPUs in Space -- Latest Project Updates](https://mlsanity.com/paper/2109.11074/) (2021)
- [3D Visual Tracking Framework with Deep Learning for Asteroid Exploration](https://mlsanity.com/paper/2111.10737/) (2021)

## Built on by (most cited first, 10 of 10)

- [Distributed Global Structure-from-Motion with a Deep Front-End](https://mlsanity.com/paper/2311.18801/) (2023)
- [Optimal DLT-based Solutions for the Perspective-n-Point](https://mlsanity.com/paper/2410.14164/) (2024)
- [Deep Monocular Hazard Detection for Safe Small Body Landing](https://mlsanity.com/paper/2301.13254/) (2023)
- [Keypoint-based Stereophotoclinometry for Characterizing and Navigating Small Bodies: A Factor Graph Approach](https://mlsanity.com/paper/2312.06865/) (2023)
- [Stereophotoclinometry Revisited](https://mlsanity.com/paper/2504.08252/) (2025)
- [Efficient Feature Description for Small Body Relative Navigation using Binary Convolutional Neural Networks](https://mlsanity.com/paper/2304.04985/) (2023)
- [CNN-based local features for navigation near an asteroid](https://mlsanity.com/paper/2309.11156/) (2023)
- [LOSTU: Fast, Scalable, and Uncertainty-Aware Triangulation](https://mlsanity.com/paper/2311.11171/) (2023)
- [Geometry-aided Vision-based Localization of Future Mars Helicopters in Challenging Illumination Conditions](https://mlsanity.com/paper/2502.09795/) (2025)
- [AstroSplat: Physics-Based Gaussian Splatting for Rendering and Reconstruction of Small Celestial Bodies](https://mlsanity.com/paper/2603.11969/) (2026)

## Abstract (arXiv)

Missions to small celestial bodies rely heavily on optical feature tracking for characterization of and relative navigation around the target body. While deep learning has led to great advancements in feature detection and description, training and validating data-driven models for space applications is challenging due to the limited availability of large-scale, annotated datasets. This paper introduces AstroVision, a large-scale dataset comprised of 115,970 densely annotated, real images of 16 different small bodies captured during past and ongoing missions. We leverage AstroVision to develop a set of standardized benchmarks and conduct an exhaustive evaluation of both handcrafted and data-driven feature detection and description methods. Next, we employ AstroVision for end-to-end training of a state-of-the-art, deep feature detection and description network and demonstrate improved performance on multiple benchmarks. The full benchmarking pipeline and the dataset will be made publicly available to facilitate the advancement of computer vision algorithms for space applications.