[36] Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, and Victor Adrian Prisacariu. Nerf-: Neural radiance fields without known camera parameters. arXiv preprint arXiv:2102.07064, 2021.
[37] Lin Yen-Chen, Pete Florence, Jonathan T Barron, Alberto Rodriguez, Phillip Isola, and Tsung-Yi Lin. Inerf: Inverting neural radiance fields for pose estimation. In Proc. IROS, pages 1323-1330. IEEE, 2021.
[38] Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, and Simon Lucey. Barf: Bundle-adjusting neural radiance fields. In Proc. CVPR, 2021.
[39] Axel Levy, Mark Matthews, Matan Sela, Gordon Wetzstein, and Dmitry Lagun. Melon: Nerf with unposed images using equivalence class estimation. arXiv preprint arXiv:2303.08096, 2023.
[40] Wenjing Bian, Zirui Wang, Kejie Li, Jiawang Bian, and Victor Adrian Prisacariu. Nope-nerf: Optimising neural radiance field with no pose prior. 2023.
[41] Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos. Orb-slam: a versatile and accurate monocular slam system. 2015.
[42] Jakob Engel, Thomas Schöps, and Daniel Cremers. Lsd-slam: Large-scale direct monocular slam. In Proc. ECCV, 2014.
[43] Zachary Teed and Jia Deng. Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras. 2021.
[44] Krishna Murthy Jatavallabhula, Ganesh Iyer, and Liam Paull. Grad-slam: Dense slam meets automatic differentiation. In Proc. ICRA. IEEE, 2020.
[45] Zihan Zhu, Songyou Peng, Viktor Larsson, Weiwei Xu, Hujun Bao, Zhaopeng Cui, Martin R Oswald, and Marc Pollefeys. Nice-slam: Neural implicit scalable encoding for slam. In Proc. CVPR, pages 12786-12796, 2022.
[46] Antoni Rosinol, John J Leonard, and Luca Carlone. Nerf-slam: Real-time dense monocular slam with neural radiance fields. arXiv preprint arXiv:2210.13641, 2022.
[47] Chi-Ming Chung, Yang-Che Tseng, Ya-Ching Hsu, Xiang-Qian Shi, Yun-Hung Hua, Jia-Fong Yeh, Wen-Chin Chen, Yi-Ting Chen, and Winston H Hsu. Orbeez-slam: A real-time monocular visual slam with orb features and nef-realized mapping. arXiv preprint arXiv:2209.13274, 2022.
[48] Edgar Sucar, Shikun Liu, Joseph Ortiz, and Andrew J Davison. imap: Implicit mapping and positioning in real-time. In Proc. ICCV, pages 6229-6238, 2021.
[49] Tinghui Zhou, Matthew Brown, Noah Snavely, and David G Lowe. Unsupervised learning of depth and ego-motion from video. In Proc. CVPR, 2017.
[50] Zhichao Yin and Jianping Shi. Geonet: Unsupervised learning of dense depth, optical flow and camera pose. In Proc. CVPR, 2018.
[51] Vitor Guizilini, Kuan-Hui Lee, Rares Ambrus, and Adrien Gaidon. Learning optical flow, depth, and scene flow without real-world labels. Proc. ICRA, 2022.
[52] Yang Fu, Ishan Misra, and Xiaolong Wang. Multiplane nerf-supervised disentanglement of depth and camera pose from videos. arXiv preprint arXiv:2210.07181, 2022.
[53] Zihang Lai, Sifei Liu, Alexei A Efros, and Xiaolong Wang. Video autoencoder: self-supervised disentanglement of static 3d structure and motion. In Proc. ICCV, 2021.
[54] Hsiao-Yu Fish Tung, Ricson Cheng, and Katerina Fragkiadaki. Learning spatial common sense with geometry-aware recurrent networks. In Proc. CVPR, 2019.
[55] Yu Chen and Gim Hee Lee. Dbarf: Deep bundle-adjusting generalizable neural radiance fields. arXiv preprint arXiv:2303.14478, 2023.
[56] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In Proc. CVPR, pages 402-419. Springer, 2020.
[57] Linyi Jin, Jianming Zhang, Yannick Hold-Geoffroy, Oliver Wang, Kevin Matzen, Matthew Sticha, and David F. Fouhey. Perspective fields for single image camera calibration. Proc. CVPR, 2023.
[58] Christopher Choy, Wei Dong, and Vladlen Koltun. Deep global registration. In Proc. CVPR, 2020.