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This paper introduces FlowMap, an end-to-end differentiable method that solves for precise camera poses, camera intrinsics, and per-frame dense depth of a video sequence.
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Engel, J., Schöps, T., Cremers, D.: Lsd-slam: Large-scale direct monocular slam. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 834–849. Springer (2014)
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Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv:1412.6980 (2014)
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Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: Orb-slam: a versatile and accurate monocular slam system. Transactions on Robotics (5), 1147–1163 (2015)
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Choy, C.B., Gwak, J., Savarese, S., Chandraker, M.: Universal correspondence network. Advances in neural information processing systems
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Schonberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4104–4113 (2016)
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Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE transactions on pattern analysis and machine intelligence
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Knapitsch, A., Park, J., Zhou, Q.Y., Koltun, V.: Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Transactions on Graphics (TOG)
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Mishchuk, A., Mishkin, D., Radenovic, F., Matas, J.: Working hard to know your neighbor’s margins: Local descriptor learning loss. Advances in Neural Information Processing Systems (NeurIPS)
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Mur-Artal, R., Tardós, J.D.: Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras. Transactions on Robotics
2017
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Ummenhofer, B., Zhou, H., Uhrig, J., Mayer, N., Ilg, E., Dosovitskiy, A., Brox, T.: Demon: Depth and motion network for learning monocular stereo. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5038–5047 (2017)
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Bloesch, M., Czarnowski, J., Clark, R., Leutenegger, S., Davison, A.J.: Codeslam—learning a compact, optimisable representation for dense visual slam. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2560–2568 (2018)
2018
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Clark, R., Bloesch, M., Czarnowski, J., Leutenegger, S., Davison, A.J.: Learning to solve nonlinear least squares for monocular stereo. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 284–299 (2018)
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DeTone, D., Malisiewicz, T., Rabinovich, A.: Superpoint: Self-supervised interest point detection and description. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. pp. 224–236 (2018)
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Luo, Z., Shen, T., Zhou, L., Zhu, S., Zhang, R., Yao, Y., Fang, T., Quan, L.: Geodesc: Learning local descriptors by integrating geometry constraints. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 168–183 (2018)
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Ono, Y., Trulls, E., Fua, P., Yi, K.M.: Lf-net: Learning local features from images. Advances in Neural Information Processing Systems (NeurIPS)
2018
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Tang, C., Tan, P.: BA-Net: Dense bundle adjustment network. arXiv preprint arXiv:1806.04807 (2018)
2018
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2018
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Zhou, H., Ummenhofer, B., Brox, T.: Deeptam: Deep tracking and mapping. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 822–838 (2018)
2018
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Liu, C., Gu, J., Kim, K., Narasimhan, S.G., Kautz, J.: Neural rgb (r) d sensing: Depth and uncertainty from a video camera. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 10986–10995 (2019)
2019
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Mildenhall, B., Srinivasan, P.P., Ortiz-Cayon, R., Kalantari, N.K., Ramamoorthi, R., Ng, R., Kar, A.: Local light field fusion: Practical view synthesis with prescriptive sampling guidelines. ACM Transactions on Graphics (TOG)
2019
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Sitzmann, V., Thies, J., Heide, F., Nießner, M., Wetzstein, G., Zollhöfer, M.: Deepvoxels: Learning persistent 3d feature embeddings. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
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Sitzmann, V., Zollhöfer, M., Wetzstein, G.: Scene representation networks: Continuous 3d-structure-aware neural scene representations. Advances in Neural Information Processing Systems (NeurIPS) (2019)
2019
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Choy, C., Dong, W., Koltun, V.: Deep global registration. In: Proc. CVPR (2020)
2020
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Czarnowski, J., Laidlow, T., Clark, R., Davison, A.J.: Deepfactors: Real-time probabilistic dense monocular SLAM. Computing Research Repository (CoRR) (2020)
2020
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Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: Representing scenes as neural radiance fields for view synthesis. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 405–421 (2020)
2020
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Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.: Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 3504–3515 (2020)
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Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., Koltun, V.: Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence (2020)
2020
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Rosinol, A., Abate, M., Chang, Y., Carlone, L.: Kimera: an open-source library for real-time metric-semantic localization and mapping. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). pp. 1689–1696. IEEE (2020)
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Sarlin, P.E., DeTone, D., Malisiewicz, T., Rabinovich, A.: Superglue: Learning feature matching with graph neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4938–4947 (2020)
2020
Cited alongside, same era.
Sarlin, P.E., DeTone, D., Malisiewicz, T., Rabinovich, A.: SuperGlue: Learning feature matching with graph neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Cited alongside, same era.
Teed, Z., Deng, J.: RAFT: Recurrent all-pairs field transforms for optical flow. In: Proceedings of the European Conference on Computer Vision (ECCV) (2020)
2020
Cited alongside, same era.
Barron, J.T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., Srinivasan, P.P.: Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In: Proceedings of the International Conference on Computer Vision (ICCV) (2021)
2021
Cited alongside, same era.
Bian, W., Wang, Z., Li, K., Bian, J., Prisacariu, V.A.: Nope-nerf: Optimising neural radiance field with no pose prior (2023)
2023
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Chan, E.R., Nagano, K., Chan, M.A., Bergman, A.W., Park, J.J., Levy, A., Aittala, M., De Mello, S., Karras, T., Wetzstein, G.: Generative novel view synthesis with 3d-aware diffusion models. Proceedings of the International Conference on 3D Vision (3DV) (2023)
2023
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Charatan, D., Li, S., Tagliasacchi, A., Sitzmann, V.: pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
2023
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Chen, Y., Lee, G.H.: Dbarf: Deep bundle-adjusting generalizable neural radiance fields. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 24–34 (June 2023)
2023
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Campos, C., Elvira, R., Rodríguez, J.J.G., M. Montiel, J.M., D. Tardós, J.: Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam. Transactions on Robotics (6), 1874–1890 (2021)
2021
Cited alongside, same era.
Jeong, Y., Ahn, S., Choy, C., Anandkumar, A., Cho, M., Park, J.: Self-calibrating neural radiance fields. In: Proceedings of the International Conference on Computer Vision (ICCV). pp. 5846–5854 (2021)
2021
Cited alongside, same era.
Kopf, J., Rong, X., Huang, J.B.: Robust consistent video depth estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1611–1621 (2021)
2021
Cited alongside, same era.
Lai, Z., Liu, S., Efros, A.A., Wang, X.: Video autoencoder: self-supervised disentanglement of static 3d structure and motion. In: Proceedings of the International Conference on Computer Vision (ICCV). pp. 9730–9740 (2021)
2021
Cited alongside, same era.
Lin, C.H., Ma, W.C., Torralba, A., Lucey, S.: Barf: Bundle-adjusting neural radiance fields. In: Proceedings of the International Conference on Computer Vision (ICCV). pp. 5741–5751 (2021)
2021
Cited alongside, same era.
Reizenstein, J., Shapovalov, R., Henzler, P., Sbordone, L., Labatut, P., Novotny, D.: Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction. In: Proceedings of the International Conference on Computer Vision (ICCV). pp. 10901–10911 (2021)
2021
Cited alongside, same era.
Sarlin, P.E., Unagar, A., Larsson, M., Germain, H., Toft, C., Larsson, V., Pollefeys, M., Lepetit, V., Hammarstrand, L., Kahl, F., Sattler, T.: Back to the Feature: Learning Robust Camera Localization from Pixels to Pose. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
2021
Cited alongside, same era.
Teed, Z., Deng, J.: Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras. Advances in Neural Information Processing Systems (NeurIPS)
2021
Cited alongside, same era.
2023
Later among the works it cites.
2023
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Du, Y., Smith, C., Tewari, A., Sitzmann, V.: Learning to render novel views from wide-baseline stereo pairs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
2023
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Fu, H., Yu, X., Li, L., Zhang, L.: Cbarf: Cascaded bundle-adjusting neural radiance fields from imperfect camera poses (2023)
2023
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Fu, Y., Liu, S., Kulkarni, A., Kautz, J., Efros, A.A., Wang, X.: Colmap-free 3d gaussian splatting (2023)
2023
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Fu, Y., Misra, I., Wang, X.: Mononerf: Learning generalizable nerfs from monocular videos without camera poses (2023)
2023
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Gao, Z., Dai, W., Zhang, Y.: Adaptive positional encoding for bundle-adjusting neural radiance fields. In: 2023 IEEE/CVF International Conference on Computer Vision (ICCV). pp. 3261–3271 (2023). https://doi.org/10.1109/ICCV51070.2023.00304
2023
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Karaev, N., Rocco, I., Graham, B., Neverova, N., Vedaldi, A., Rupprecht, C.: CoTracker: It is better to track together (2023)
2023
Later among the works it cites.
2023
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Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics (ToG)
2023
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Li, Z., Müller, T., Evans, A., Taylor, R.H., Unberath, M., Liu, M.Y., Lin, C.H.: Neuralangelo: High-fidelity neural surface reconstruction. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
2023
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Lindenberger, P., Sarlin, P.E., Pollefeys, M.: LightGlue: Local Feature Matching at Light Speed. In: Proceedings of the International Conference on Computer Vision (ICCV) (2023)
2023
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Liu, Y.L., Gao, C., Meuleman, A., Tseng, H.Y., Saraf, A., Kim, C., Chuang, Y.Y., Kopf, J., Huang, J.B.: Robust dynamic radiance fields. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13–23 (2023)
2023
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Meuleman, A., Liu, Y.L., Gao, C., Huang, J.B., Kim, C., Kim, M.H., Kopf, J.: Progressively optimized local radiance fields for robust view synthesis. In: CVPR (2023)
2023
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Park, K., Henzler, P., Mildenhall, B., Barron, J.T., Martin-Brualla, R.: Camp: Camera preconditioning for neural radiance fields. ACM Transactions on Graphics (TOG)
2023
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Smith, C., Du, Y., Tewari, A., Sitzmann, V.: Flowcam: Training generalizable 3d radiance fields without camera poses via pixel-aligned scene flow. Advances in Neural Information Processing Systems (NeurIPS) (2023)
2023
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Tancik, M., Weber, E., Ng, E., Li, R., Yi, B., Kerr, J., Wang, T., Kristoffersen, A., Austin, J., Salahi, K., Ahuja, A., McAllister, D., Kanazawa, A.: Nerfstudio: A modular framework for neural radiance field development. In: ACM Transactions on Graphics (TOG) (2023)
2023
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Tewari, A., Yin, T., Cazenavette, G., Rezchikov, S., Tenenbaum, J.B., Durand, F., Freeman, W.T., Sitzmann, V.: Diffusion with forward models: Solving stochastic inverse problems without direct supervision. Advances in Neural Information Processing Systems (NeurIPS) (2023)
2023
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2023
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Wu, X., Xu, J., Zhang, X., Bao, H., Huang, Q., Shen, Y., Tompkin, J., Xu, W.: Scanerf: Scalable bundle-adjusting neural radiance fields for large-scale scene rendering. ACM Transactions on Graphics (TOG) (2023)
2023
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2023
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2023
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Matsuki, H., Murai, R., Kelly, P.H.J., Davison, A.J.: Gaussian Splatting SLAM. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2024)
2024
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Wewer, C., Raj, K., Ilg, E., Schiele, B., Lenssen, J.E.: latentsplat: Autoencoding variational gaussians for fast generalizable 3d reconstruction. In: arXiv (2024)
2024
Closest in time.