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We propose a generalizable neural radiance fields - MonoNeRF, that can be trained on large-scale monocular videos of moving in static scenes without any ground-truth annotations of depth and camera poses.
Least-squares estimation of transformation parameters between two point patterns
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Unsupervised learning of 3d structure from images
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Deeper depth prediction with fully convolutional residual networks
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
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Unsupervised monocular depth estimation with left-right consistency
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Sfm-net: Learning of structure and motion from video
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Neural scene representation and rendering
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Multimodal unsupervised image-to-image translation
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Jha, A. H., Anand, S., Singh, M., and Veeravasarapu, V · 2018
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Megadepth: Learning single-view depth prediction from internet photos
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Mvdepthnet: Real-time multiview depth estimation neural network
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Wiles, O., Koepke, A., and Zisserman, A · 2018
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Lego: Learning edge with geometry all at once by watching videos
Yang, Z., Wang, P., Wang, Y., Xu, W., and Nevatia, R · 2018
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Geonet: Unsupervised learning of dense depth, optical flow and camera pose
Yin, Z. and Shi, J · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Atlas: End-to-end 3d scene reconstruction from posed images
Murez, Z., As, T. v., Bartolozzi, J., Sinha, A., Badrinarayanan, V., and Rabinovich, A · 2020
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Self-supervised viewpoint learning from image collections
Mustikovela, S. K., Jampani, V., Mello, S. D., Liu, S., Iqbal, U., Rother, C., and Kautz, J · 2020
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Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
Niemeyer, M., Mescheder, L., Oechsle, M., and Geiger, A · 2020
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Swapping autoencoder for deep image manipulation
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Adversarial latent autoencoders
Pidhorskyi, S., Adjeroh, D. A., and Doretto, G · 2020
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Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
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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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Visual object networks: Image generation with disentangled 3d representations
Zhu, J.-Y., Zhang, Z., Zhang, C., Wu, J., Torralba, A., Tenenbaum, J., and Freeman, B · 2018
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Digging into self-supervised monocular depth estimation
Godard, C., Mac Aodha, O., Firman, M., and Brostow, G. J · 2019
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Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras
Gordon, A., Li, H., Jonschkowski, R., and Angelova, A · 2019
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Video representation learning by dense predictive coding
Han, T., Xie, W., and Zisserman, A · 2019
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Multi-view stereo by temporal nonparametric fusion
Hou, Y., Kannala, J., and Solin, A · 2019
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Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., and Koltun, V · 2020
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Graf: Generative radiance fields for 3d-aware image synthesis
Schwarz, K., Liao, Y., Niemeyer, M., and Geiger, A · 2020
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Single-view view synthesis with multiplane images
Tucker, R. and Snavely, N · 2020
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Synsin: End-to-end view synthesis from a single image
Wiles, O., Gkioxari, G., Szeliski, R., and Johnson, J · 2020
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P 2 net: Patch-match and plane-regularization for unsupervised indoor depth estimation
Yu, Z., Jin, L., and Gao, S · 2020
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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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Video autoencoder: self-supervised disentanglement of static 3d structure and motion
Lai, Z., Liu, S., Efros, A. A., and Wang, X · 2021
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Mine: Towards continuous depth mpi with nerf for novel view synthesis
Li, J., Feng, Z., She, Q., Ding, H., Wang, C., and Lee, G. H · 2021
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Nerf in the wild: Neural radiance fields for unconstrained photo collections
Martin-Brualla, R., Radwan, N., Sajjadi, M. S., Barron, J. T., Dosovitskiy, A., and Duckworth, D · 2021
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Gnerf: Gan-based neural radiance field without posed camera
Meng, Q., Chen, A., Luo, H., Wu, M., Su, H., Xu, L., He, X., and Yu, J · 2021
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Pixelsynth: Generating a 3d-consistent experience from a single image
Rockwell, C., Fouhey, D. F., and Johnson, J · 2021
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Grf: Learning a general radiance field for 3d representation and rendering
Trevithick, A. and Yang, B · 2021
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The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth
Watson, J., Aodha, O. M., Prisacariu, V., Brostow, G., and Firman, M · 2021
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Volume rendering of neural implicit surfaces
Yariv, L., Gu, J., Kasten, Y., and Lipman, Y · 2021
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Learning to recover 3d scene shape from a single image
Yin, W., Zhang, J., Wang, O., Niklaus, S., Mai, L., Chen, S., and Shen, C · 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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Ners: neural reflectance surfaces for sparse-view 3d reconstruction in the wild
Zhang, J., Yang, G., Tulsiani, S., and Ramanan, D · 2021
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In-place scene labelling and understanding with implicit scene representation
Zhi, S., Laidlow, T., Leutenegger, S., and Davison, A. J · 2021
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3d neural scene representations for visuomotor control
Li, Y., Li, S., Sitzmann, V., Agrawal, P., and Torralba, A · 2022
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Se (3)-equivariant relational rearrangement with neural descriptor fields
Simeonov, A., Du, Y., Yen-Chen, L., Rodriguez, A., Kaelbling, L. P., Lozano-Perez, T., and Agrawal, P · 2022
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Wang, P., Chen, X., Chen, T., Venugopalan, S., Wang, Z., et al · 2022
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Geometry-biased transformers for novel view synthesis
Venkat, N., Agarwal, M., Singh, M., and Tulsiani, S · 2023
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