A Review of Image-based Small Planetary Body Shape Modelling
- 1Institute of Geodesy and Geoinformation Science, Technische Universität Berlin, Germany (hao.chen.2@campus.tu-berlin.de)
- 2Institute of Planetary Research, German Aerospace Center (DLR), Berlin, Germany
- 3Institute of Space Technology & Space Applications (LRT 9.1), University of the Bundeswehr Munich, Neubiberg, Germany
- 4Instituto de Astrofísica de Andalucía (IAA-CSIC), Granada, Spain
Images are a powerful data source for modelling the shape of small planetary bodies, e.g., asteroids, comets, and many planetary satellites. The traditional stereo-photogrammetry (SPG), stereo-photoclinometry (SPC) methods have recently been joined by Deep Learning (DL) methods to achieve shape modelling. SPG and SPC methods have been applied previously to support various exploration missions, such as NASA OSIRIS-REx mission (Palmer et al., 2022), ESA Rosetta mission (Preusker et al., 2015), JAXA Hayabusa and Hayabusa2 missions (Gaskell et al., 2008; Watanabe et al., 2019), etc. To effectively achieve accurate 3D reconstruction, SPG methods require images taken under similar illumination geometry, as well as sufficient viewing coverage from different perspectives, while SPC methods prefer images involving illumination conditions.
Recently developed DL methods are divided into two modes. One of them involves using DL techniques to replace specific steps of the traditional methods in order to enhance shape modeling accuracy. For example, the matching process in the SPG method may be replaced by the DL technique to improve the matching accuracy (Chen et al., 2023). In contrast, the so-called “neural implicit methods” make full use of DL methods to replace the SPG method once accuracy in positions and orientations is attained (Chen et al., 2024). This approach can be trained and derive shape models end-to-end without any additional supporting work steps, showing a high potential as a complementary method for SPG and SPC. It is worth mentioning that the neural implicit method only needs a small number of images to train the model.
References:
Preusker et al., 2015. Shape model, reference system definition, and cartographic mapping standards for comet 67P/Churyumov-Gerasimenko–Stereo- photogrammetric analysis of Rosetta/OSIRIS image data. A & A 583, A33. https://doi.org/10.1051/0004-6361/201526349.
Gaskell et al., 2008. Characterizing and navigating small bodies with imaging data. Meteorit. Planet. Sci. 43 (6), 1049–1061. https://doi.org/10.1111/j.1945-5100.2008.tb00692.x
Watanabe et al., 2019. Hayabusa2 arrives at the carbonaceous asteroid 162173 Ryugu--A spinning top–shaped rubble pile. Science 364 (6437), 268–272. https://www.science.org/doi/10.1126/science.aav8032.
Palmer et al., 2022. Practical stereophotoclinometry for modeling shape and topography on planetary missions. The Planetary Sci. J. 3 (5), 102. https://doi.org/10.3847/PSJ/ac460f.
Chen et al., 2023. A new shape model of the bilobate comet 67P/Churyumov-Gerasimenko. Icarus. 401, 115566. https://doi.org/10.1016/j.icarus.2023.115566.
Chen et al., 2024. Neural implicit shape modeling for small planetary bodies from multi-view images using a mask-based classification sampling strategy. ISPRS Journal of Photogrammetry and Remote Sensing. 212, 122-145. https://doi.org/10.1016/j.isprsjprs.2024.04.029.
How to cite: Chen, H., Willner, K., Hu, X., Xiao, H., Gläser, P., and Oberst, J.: A Review of Image-based Small Planetary Body Shape Modelling, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-570, https://doi.org/10.5194/epsc2024-570, 2024.
