| --- |
| license: cc-by-4.0 |
| task_categories: |
| - robotics |
| - feature-extraction |
| language: |
| - en |
| tags: |
| - Space |
| - Structure-from-Motion |
| - SfM |
| - SLAM |
| - Asteroid |
| - Stereophotoclinometry |
| pretty_name: Photoclinometry-from-Motion (PhoMo) |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: site |
| dtype: string |
| - name: body |
| dtype: string |
| - name: fits_path |
| dtype: string |
| - name: npy_path |
| dtype: string |
| - name: id |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 92236737 |
| num_examples: 99 |
| download_size: 92237643 |
| dataset_size: 92236737 |
| --- |
| |
| <div align="center"> |
| <img src="assets/phomo.png" alt="logo" width="400"> |
| <h1>Photoclinometry-from-Motion (PhoMo)</h1> |
|
|
| <a href="https://huggingface.co/datasets/travisdriver/phomo-data"><img src="https://img.shields.io/badge/🤗-Hugging%20Face-yellow.svg" alt="HuggingFace"></a> |
| <a href="https://arxiv.org/abs/2504.08252"><img src="https://img.shields.io/badge/arXiv-2504.08252-b31b1b" alt="arXiv"></a> |
|
|
| [Travis Driver](https://travisdriver.github.io/), [Andrew Vaughan](https://www.linkedin.com/in/andrewtvaughan/), [Yang Cheng](https://www-robotics.jpl.nasa.gov/who-we-are/people/yang_cheng/), [Adnan Ansar](https://www-robotics.jpl.nasa.gov/who-we-are/people/adnan_ansar/), [John Christian](https://ae.gatech.edu/directory/person/john-christian), [Panagiotis Tsiotras](https://ae.gatech.edu/directory/person/panagiotis-tsiotras) |
| </div> |
|
|
| #### This is the official repository for [Stereophotoclinometry Revisited](https://arxiv.org/abs/2504.08252), which is currently under review for publication to AIAA's [Journal of Guidance, Control, and Dynamics (JGCD)](https://arc.aiaa.org/loi/jgcd) |
|
|
| **Photoclinometry-from-Motion (PhoMo)** is a framework for _autonomous_ image-based surface reconstruction and characterization of small celestial bodies. PhoMo integrates photoclinometry into a structure-from-motion (SfM) pipeline that leverages deep learning-based keypoint extraction and matching (i.e., [RoMa](https://github.com/Parskatt/RoMa)) to enable _simultaneous_ optimization of the spacecraft pose, landmark positions, Sun vectors, and surface normals and albedos. |
|
|
| If you find our datasets or results useful for your research, please use the following citation: |
|
|
| ```bibtex |
| @article{driver2025phomo, |
| title={Stereophotoclinometry Revisited}, |
| author={Driver, Travis and Vaughan, Andrew and Cheng, Yang, and Ansar, Adnan and Christian, John and Tsiotras, Panagiotis}, |
| journal={arXiv:2504.08252}, |
| year={2025}, |
| pages={1--45} |
| } |
| ``` |
|
|
| <!--- |
|
|
| ## Dataset Structure |
|
|
| Each directory contains a subdirectory for each of the sites used in the paper, i.e., Cornelia (`cornelia/`), Ahuna Mons (`ahunamons/`), and Ikapati (`ikapati/`). |
|
|
| `images/`: Contains the input images for each site (i.e., Cornelia, Ahuna Mons, and Ikapati) in multiple formats. |
|
|
| - `*_calib.npy`: Radiometrically calibrated to units of reflectance (L/F). |
| - `*_uncalib.npy`: |
| - `*.png`: |
| ---> |
|
|
