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We consider the problem of novel view synthesis from unposed images in a single feed-forward.
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Yi, K. M., Trulls, E., Lepetit, V., and Fua, P · 2016
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Relative camera pose estimation using convolutional neural networks
Melekhov, I., Ylioinas, J., Kannala, J., and Rahtu, E · 2017
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Unsupervised learning of depth and ego-motion from video
Zhou, T., Brown, M., Snavely, N., and Lowe, D. G · 2017
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Superpoint: Self-supervised interest point detection and description
DeTone, D., Malisiewicz, T., and Rabinovich, A · 2018
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Mvsnet: Depth inference for unstructured multi-view stereo
Yao, Y., Luo, Z., Li, S., Fang, T., and Quan, L · 2018
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3dfeat-net: Weakly supervised local 3d features for point cloud registration
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Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
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Stereo magnification: Learning view synthesis using multiplane images
Zhou, T., Tucker, R., Flynn, J., Fyffe, G., and Snavely, N · 2018
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Magsac: marginalizing sample consensus
Barath, D., Matas, J., and Noskova, J · 2019
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Fully convolutional geometric features
Choy, C., Park, J., and Koltun, V · 2019
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Superglue: Learning feature matching with graph neural networks
Sarlin, P.-E., DeTone, D., Malisiewicz, T., and Rabinovich, A · 2020
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Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields
Barron, J. T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., and Srinivasan, P. P · 2021
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Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo
Chen, A., Xu, Z., Zhao, F., Zhang, X., Xiang, F., Yu, J., and Su, H · 2021
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Cats: Cost aggregation transformers for visual correspondence
Cho, S., Hong, S., Jeon, S., Lee, Y., Sohn, K., and Kim, S · 2021
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Deep matching prior: Test-time optimization for dense correspondence
Hong, S. and Kim, S · 2021
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Self-calibrating neural radiance fields
Jeong, Y., Ahn, S., Choy, C., Anandkumar, A., Cho, M., and Park, J · 2021
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Barf: Bundle-adjusting neural radiance fields
Lin, C.-H., Ma, W.-C., Torralba, A., and Lucey, S · 2021
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Infinite nature: Perpetual view generation of natural scenes from a single image
Liu, A., Tucker, R., Jampani, V., Makadia, A., Snavely, N., and Kanazawa, A · 2021
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Nerf: Representing scenes as neural radiance fields for view synthesis
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., and Ng, R · 2021
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Light field networks: Neural scene representations with single-evaluation rendering
Sitzmann, V., Rezchikov, S., Freeman, B., Tenenbaum, J., and Durand, F · 2021
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Loftr: Detector-free local feature matching with transformers
Sun, J., Shen, Z., Wang, Y., Bao, H., and Zhou, X · 2021
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Nerf–: Neural radiance fields without known camera parameters
Wang, Z., Wu, S., Xie, W., Chen, M., and Prisacariu, V. A · 2021
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pixelnerf: Neural radiance fields from one or few images
Yu, A., Ye, V., Tancik, M., and Kanazawa, A · 2021
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Map-free visual relocalization: Metric pose relative to a single image
Arnold, E., Wynn, J., Vicente, S., Garcia-Hernando, G., Monszpart, A., Prisacariu, V., Turmukhambetov, D., and Brachmann, E · 2022
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Dust3r: Geometric 3d vision made easy
Wang, S., Leroy, V., Cabon, Y., Chidlovskii, B., and Revaud, J · 2023
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Generalized differentiable ransac
Wei, T., Patel, Y., Shekhovtsov, A., Matas, J., and Barath, D · 2023
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Contranerf: Generalizable neural radiance fields for synthetic-to-real novel view synthesis via contrastive learning
Yang, H., Hong, L., Li, A., Hu, T., Li, Z., Lee, G. H., and Wang, L · 2023
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Metric3d: Towards zero-shot metric 3d prediction from a single image
Yin, W., Zhang, C., Chen, H., Cai, Z., Yu, G., Wang, K., Chen, X., and Shen, C · 2023
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Cross-view completion models are zero-shot correspondence estimators
An, H., Kim, J., Park, S., Jung, J., Han, J., Hong, S., and Kim, S · 2024
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Mip-nerf 360: Unbounded anti-aliased neural radiance fields
Barron, J. T., Mildenhall, B., Verbin, D., Srinivasan, P. P., and Hedman, P · 2022
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Cats++: Boosting cost aggregation with convolutions and transformers
Cho, S., Hong, S., and Kim, S · 2022
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FlashAttention: Fast and memory-efficient exact attention with IO-awareness
Dao, T., Fu, D. Y., Ermon, S., Rudra, A., and Ré, C · 2022
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Geonerf: Generalizing nerf with geometry priors
Johari, M. M., Lepoittevin, Y., and Fleuret, F · 2022
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The 8-point algorithm as an inductive bias for relative pose prediction by vits
Rockwell, C., Johnson, J., and Fouhey, D. F · 2022
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Gmflow: Learning optical flow via global matching
Xu, H., Zhang, J., Cai, J., Rezatofighi, H., and Tao, D · 2022
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Relpose: Predicting probabilistic relative rotation for single objects in the wild
Zhang, J. Y., Ramanan, D., and Tulsiani, S · 2022
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Depth pro: Sharp monocular metric depth in less than a second
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Chen, Y., Xu, H., Zheng, C., Zhuang, B., Pollefeys, M., Geiger, A., Cham, T.-J., and Cai, J · 2024
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Roma: Robust dense feature matching
Edstedt, J., Sun, Q., Bökman, G., Wadenbäck, M., and Felsberg, M · 2024
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Unifying feature and cost aggregation with transformers for semantic and visual correspondence
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Repurposing diffusion-based image generators for monocular depth estimation
Ke, B., Obukhov, A., Huang, S., Metzger, N., Daudt, R. C., and Schindler, K · 2024
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Splatam: Splat, track & map 3d gaussians for dense rgb-d slam
Keetha, N., Karhade, J., Jatavallabhula, K. M., Yang, G., Scherer, S., Ramanan, D., and Luiten, J · 2024
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Grounding image matching in 3d with mast3r
Leroy, V., Cabon, Y., and Revaud, J · 2024
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Ggrt: Towards generalizable 3d gaussians without pose priors in real-time
Li, H., Gao, Y., Wu, C., Zhang, D., Dai, Y., Zhao, C., Feng, H., Ding, E., Wang, J., and Han, J · 2024
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Dl3dv-10k: A large-scale scene dataset for deep learning-based 3d vision
Ling, L., Sheng, Y., Tu, Z., Zhao, W., Xin, C., Wan, K., Yu, L., Guo, Q., Yu, Z., Lu, Y., et al · 2024
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Unidepth: Universal monocular metric depth estimation
Piccinelli, L., Yang, Y.-H., Sakaridis, C., Segu, M., Li, S., Van Gool, L., and Yu, F · 2024
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Far: Flexible accurate and robust 6dof relative camera pose estimation
Rockwell, C., Kulkarni, N., Jin, L., Park, J. J., Johnson, J., and Fouhey, D. F · 2024
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Splatt3r: Zero-shot gaussian splatting from uncalibarated image pairs
Smart, B., Zheng, C., Laina, I., and Prisacariu, V. A · 2024
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Splatter image: Ultra-fast single-view 3d reconstruction
Szymanowicz, S., Rupprecht, C., and Vedaldi, A · 2024
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Vggsfm: Visual geometry grounded deep structure from motion
Wang, J., Karaev, N., Rupprecht, C., and Novotny, D · 2024
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Depth anything: Unleashing the power of large-scale unlabeled data
Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., and Zhao, H · 2024
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No pose, no problem: Surprisingly simple 3d gaussian splats from sparse unposed images
Ye, B., Liu, S., Xu, H., Li, X., Pollefeys, M., Yang, M.-H., and Peng, S · 2024
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Mip-splatting: Alias-free 3d gaussian splatting
Yu, Z., Chen, A., Huang, B., Sattler, T., and Geiger, A · 2024
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On the continuity of rotation representations in neural networks
Zhou, Y., Barnes, C., Jingwan, L., Jimei, Y., and Hao, L · 2024
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