Open Access
Issue
A&A
Volume 687, July 2024
Article Number A278
Number of page(s) 12
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202450053
Published online 22 July 2024
  1. Abe, S., Mukai, T., Hirata, N., et al. 2006, Science, 312, 1344 [NASA ADS] [CrossRef] [Google Scholar]
  2. Al Asad, M., Philpott, L., Johnson, C., et al. 2021, Planet. Sci. J., 2, 82 [NASA ADS] [CrossRef] [Google Scholar]
  3. Barnouin, O., Ernst, C., & Daly, R. 2018 in Planetary Science Informatics and Data Analytics Conference, 2082, 6043 [NASA ADS] [Google Scholar]
  4. Barnouin, O., Daly, M., Palmer, E., et al. 2019, Nat. Geosci., 12, 247 [NASA ADS] [CrossRef] [Google Scholar]
  5. Barnouin, O., Daly, M., Palmer, E., et al. 2020, Planet. Space Sci., 180, 104764 [NASA ADS] [CrossRef] [Google Scholar]
  6. Barron, J. T., Mildenhall, B., Tancik, M., et al. 2021, in Proceedings of the IEEE/CVF International Conference on Computer Vision, 5855 [Google Scholar]
  7. Barron, J. T., Mildenhall, B., Verbin, D., Srinivasan, P. P., & Hedman, P. 2022, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5470 [Google Scholar]
  8. Chen, Z., & Zhang, H. 2019, in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 5939 [Google Scholar]
  9. Coughlan, J. M., & Yuille, A. L. 1999, Proceedings of the seventh IEEE international conference on computer vision, 2, 941 [CrossRef] [Google Scholar]
  10. Daly, M., Barnouin, O., Seabrook, J., et al. 2020, Sci. Adv., 6, eabd3649 [NASA ADS] [CrossRef] [Google Scholar]
  11. Demura, H., Kobayashi, S., Nemoto, E., et al. 2006, Science, 312, 1347 [NASA ADS] [CrossRef] [Google Scholar]
  12. Do, P. N. B., & Nguyen, Q. C. 2019, in 19th International Symposium on Communications and Information Technologies (ISCIT), 138 [Google Scholar]
  13. Edmundson, K., Becker, K., Becker, T., et al. 2020, Remote Sens. Spatial Inform. Sci., 3, 587 [Google Scholar]
  14. Festou, M., Keller, H. U., & Weaver, H. A. 2004, Comets II (Tucson: University of Arizona Press) [Google Scholar]
  15. Fujiwara, A., Kawaguchi, J., Yeomans, D., et al. 2006, Science, 312, 1330 [NASA ADS] [CrossRef] [Google Scholar]
  16. Gaskell, R. W. 2012, AAS/Div. Planet. Sci. Meeting Abstracts, 44, 209 [Google Scholar]
  17. Gaskell, R., Barnouin-Jha, O., Scheeres, D., et al. 2006, in AIAA/AAS Astrody-namics Specialist Conference and Exhibit, 6660 [Google Scholar]
  18. Gaskell, R., Barnouin-Jha, O., Scheeres, D. J., et al. 2008, Meteor. Planet. Sci., 43, 1049 [NASA ADS] [CrossRef] [Google Scholar]
  19. Gaskell, R., Barnouin, O., Daly, M., et al. 2023, Planet. Sci. J., 4, 63 [NASA ADS] [CrossRef] [Google Scholar]
  20. Giese, B., Neukum, G., Roatsch, T., Denk, T., & Porco, C. C. 2006, Planet. Space Sci., 54, 1156 [NASA ADS] [CrossRef] [Google Scholar]
  21. Gropp, A., Yariv, L., Haim, N., Atzmon, M., & Lipman, Y. 2020, in Proceedings of Machine Learning and Systems 2020, 3569 [Google Scholar]
  22. Guo, H., Peng, S., Lin, H., et al. 2022, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5511 [Google Scholar]
  23. Hartley, R., & Zisserman, A. 2003, Multiple View Geometry in Computer Vision (Cambridge: Cambridge University press) [Google Scholar]
  24. Hirata, N., Sugiyama, T., Hirata, N., et al. 2020, Annual Lunar Planet. Sci. Conf., 2326, 2015 [Google Scholar]
  25. Jung, H., Ju, J., Jung, M., et al. 2018 in Proceedings of the AAAI Conference on Artificial Intelligence, 32 [Google Scholar]
  26. Kim, J., Lin, S.-Y., & Xiao, H. 2023, Remote Sens., 15, 2954 [NASA ADS] [CrossRef] [Google Scholar]
  27. Lauretta, D., Balram-Knutson, S., Beshore, E., et al. 2017, Space Sci. Rev., 212, 925 [CrossRef] [Google Scholar]
  28. Lauretta, D., Adam, C., Allen, A., et al. 2022, Science, 377, 285 [NASA ADS] [CrossRef] [Google Scholar]
  29. Li, Z., Müller, T., Evans, A., et al. 2023, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8456 [Google Scholar]
  30. Liu, W. C., & Wu, B. 2020, ISPRS J. Photogramm. Remote Sens., 159, 153 [NASA ADS] [CrossRef] [Google Scholar]
  31. Liu, W. C., & Wu, B. 2021, ISPRS J. Photogramm. Remote Sens., 182, 208 [NASA ADS] [CrossRef] [Google Scholar]
  32. Liu, W. C., Wu, B., & Wöhler, C. 2018, ISPRS J. Photogramm. Remote Sens., 136, 58 [NASA ADS] [CrossRef] [Google Scholar]
  33. Liu, Z., Feng, Y., Black, M. J., et al. 2023, in International Conference on Learning Representations, [arXiv:2303.08133] [Google Scholar]
  34. Long, J., & Wu, F. 2019, J. Guidance Control Dyn., 42, 1195 [NASA ADS] [CrossRef] [Google Scholar]
  35. Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., & Geiger, A. 2019, in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 4460 [Google Scholar]
  36. Mildenhall, B., Srinivasan, P. P., Tancik, M., et al. 2021, Commun. ACM, 65, 99 [Google Scholar]
  37. Müller, T., Evans, A., Schied, C., & Keller, A. 2022, ACM Transactions on Graphics, 41, 1 [CrossRef] [Google Scholar]
  38. Oechsle, M., Peng, S., & Geiger, A. 2021, in Proceedings of the IEEE/CVF International Conference on Computer Vision, 5589 [Google Scholar]
  39. Palmer, E. E., Head, J. N., Gaskell, R. W., Sykes, M. V., & McComas, B. 2016, Earth Space Sci., 3, 488 [NASA ADS] [CrossRef] [Google Scholar]
  40. Palmer, E. E., Gaskell, R., Daly, M. G., et al. 2022, Planet. Sci. J., 3, 102 [NASA ADS] [CrossRef] [Google Scholar]
  41. Park, J. J., Florence, P., Straub, J., Newcombe, R., & Lovegrove, S. 2019, in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 165 [Google Scholar]
  42. Preusker, F., Scholten, F., Matz, K.-D., et al. 2015, A&A, 583, A33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  43. Preusker, F., Scholten, F., Elgner, S., et al. 2019, A&A, 632, L4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  44. Remondino, F., Spera, M. G., Nocerino, E., Menna, F., & Nex, F. 2014, Photogramm. Record, 29, 144 [CrossRef] [Google Scholar]
  45. Saito, J., Miyamoto, H., Nakamura, R., et al. 2006, Science, 312, 1341 [NASA ADS] [CrossRef] [Google Scholar]
  46. Scheeres, D. J., Hesar, S. G., Tardivel, S., et al. 2016, Icarus, 276, 116 [NASA ADS] [CrossRef] [Google Scholar]
  47. Scholten, F., Preusker, F., Elgner, S., et al. 2019, A&A, 632, L5 [Google Scholar]
  48. Schonberger, J. L., & Frahm, J.-M. 2016, in Proceedings of the IEEE conference on computer vision and pattern recognition, 4104 [Google Scholar]
  49. Schönberger, J. L., Zheng, E., Frahm, J.-M., & Pollefeys, M. 2016, in Computer Vision-ECCV 2016, Proceedings, Springer, Part III, 501 [CrossRef] [Google Scholar]
  50. Tewari, A., Thies, J., Mildenhall, B., et al. 2022 in Computer Graphics Forum, Wiley Online Library, 703 [CrossRef] [Google Scholar]
  51. Ullman, S. 1979, Proc. R. Soc. London Ser. B Biol. Sci., 203, 405 [NASA ADS] [Google Scholar]
  52. Wang, P., Liu, L., Liu, Y., et al. 2021a, arXiv e-prints [arXiv:2106.10689] [Google Scholar]
  53. Wang, Z., Wu, S., Xie, W., Chen, M., & Prisacariu, V. A. 2021b, arXiv e-prints [arXiv:2102.07064] [Google Scholar]
  54. Wang, Y., Skorokhodov, I., & Wonka, P. 2022, Adv. Neural Inform. Process. Syst., 35, 1966 [Google Scholar]
  55. Wang, Y., Han, Q., Habermann, M., et al. 2023, in Proceedings of the IEEE/CVF International Conference on Computer Vision, 3295 [Google Scholar]
  56. Watanabe, S., Hirabayashi, M., Hirata, N., et al. 2019, Science, 364, 268 [NASA ADS] [Google Scholar]
  57. Weirich, J., Palmer, E. E., Daly, M. G., et al. 2022, Planet. Sci. J., 3, 103 [NASA ADS] [CrossRef] [Google Scholar]
  58. Wu, B. 2017, International Encyclopedia of Geography; American Cancer Society: Atlanta, GA, USA, 1 [Google Scholar]
  59. Wu, B., Liu, W. C., Grumpe, A., & Wöhler, C. 2018, ISPRS J. Photogramm. Remote Sens., 140, 3 [NASA ADS] [CrossRef] [Google Scholar]
  60. Yan, Q., Wang, Q., Zhao, K., et al. 2024 in Proceedings of the AAAI Conference on Artificial Intelligence, 38, 6440 [CrossRef] [Google Scholar]
  61. Yariv, L., Kasten, Y., Moran, D., et al. 2020, Adv. Neural Inform. Process. Syst., 33, 2492 [Google Scholar]
  62. Yariv, L., Gu, J., Kasten, Y., & Lipman, Y. 2021, Adv. Neural Inform. Process. Syst., 34, 4805 [Google Scholar]
  63. Yen-Chen, L., Florence, P., Barron, J. T., et al. 2021, in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 1323 [Google Scholar]
  64. Yeomans, D., Antreasian, P., Cheng, A., et al. 1999, Science, 285, 560 [NASA ADS] [CrossRef] [Google Scholar]
  65. Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. 2018, in Proceedings of the IEEE conference on computer vision and pattern recognition, 586 [Google Scholar]
  66. Zhang, K., Riegler, G., Snavely, N., & Koltun, V. 2020, arXiv e-prints [arXiv:2010.07492] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.