3D Scene Reconstruction with Neural Radiance Fields (NeRF) Considering Dynamic Illumination Conditions

Olena Kolodiazhna, Volodymyr Savin, Mykhailo Uss and Nataliia Kussul

References:

1. L. Tardon, I. Barbancho, and C. Alberola-L´opez, Markov Random Fields in the Context of Stereo Vision, 01 2011.
2. S. Zhu and L. Yan, “Local stereo matching algorithm with efficient matching cost and adaptive guided image filter,” vol. 33, no. 9, 2017. [Online]. Available: https://doi.org/10.1007/s00371-016-1264-6
3. M. Bleyer, C. Rhemann, and C. Rother, “Patchmatch stereo - stereo matching with slanted support windows,” in BMVC, January 2011. [Online]. Available: https://www.microsoft.com/enus/ research/publication/patchmatch-stereo-stereomatching- with-slanted-support-windows/
4. H. Laga, L. V. Jospin, F. Boussaid, and M. Bennamoun, “A survey on deep learning techniques for stereo-based depth estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 4, pp. 1738–1764, apr 2022.
5. J. Watson, O. M. Aodha, D. Turmukhambetov, G. J. Brostow, and M. Firman, “Learning stereo from single images.” Berlin, Heidelberg: Springer-Verlag, 2020. [Online]. Available: https://doi.org/10.1007/978-3- 030-58452-8 42
6. B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” in ECCV, 2020.
7. A. Moreau, N. Piasco, D. Tsishkou, B. Stanciulescu, and A. de La Fortelle, “Lens: Localization enhanced by nerf synthesis,” 2021. [Online]. Available: https://arxiv.org/abs/2110.06558
8. C. Godard, O. Mac Aodha, and G. J. Brostow, “Unsupervised monocular depth estimation with left-right consistency,” 2016. [Online]. Available: https://arxiv.org/abs/1609.03677
9. H. Li, A. Gordon, H. Zhao, V. Casser, and A. Angelova, “Unsupervised monocular depth learning in dynamic scenes,” in Proceedings of the 2020 Conference on Robot Learning, ser. Proceedings of Machine Learning Research, J. Kober, F. Ramos, and C. Tomlin, Eds., vol. 155. PMLR, 16–18 Nov 2021, pp. 1908–1917. [Online]. Available: https://proceedings.mlr.press/v155/li21a.html
10. K. Wang, Z. Zhang, Z. Yan, X. Li, B. Xu, J. Li, and J. Yang, “Regularizing nighttime weirdness: Efficient self-supervised monocular depth estimation in the dark,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2021, pp. 16 055–16 064.
11. A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Niesner, “Scannet: Richlyannotated 3d reconstructions of indoor scenes,” in Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, 2017.
12. M. Waechter, N. Moehrle, and M. Goesele, “Let there be color! large-scale texturing of 3d reconstructions,” in Computer Vision – ECCV 2014, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds. Cham: Springer International Publishing, 2014, pp. 836–850.
13. C. M. Jiang, A. Sud, A. Makadia, J. Huang, M. Niesner, and T. Funkhouser, “Local implicit grid representations for 3d scenes,” in Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2020.
14. E. Penner and L. Zhang, “Soft 3d reconstruction for view synthesis,” vol. 36, no. 6, 2017.
15. C.-H. Lin, W.-C. Ma, A. Torralba, and S. Lucey, “Barf: Bundle-adjusting neural radiance fields,” in IEEE International Conference on Computer Vision (ICCV), 2021.