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In recent years, neural implicit surface reconstruction methods have become popular for multi-view 3D reconstruction.
A volumetric method for building complex models from range images
B. Curless and M. Levoy · 1996
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Photorealistic scene reconstruction by voxel coloring
S. Seitz and C. Dyer · 1997
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Poxels: Probabilistic voxelized volume reconstruction
J. D. Bonet and P. Viola · 1999
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Manhattan world: Compass direction from a single image by bayesian inference
J. M. Coughlan and A. L. Yuille · 1999
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A theory of shape by space carving
K. N. Kutulakos and S. M. Seitz · 2000
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A probabilistic framework for surface reconstruction from multiple images
M. Agrawal and L. S. Davis · 2001
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A probabilistic framework for space carving
A. Broadhurst, T. W. Drummond, and R. Cipolla · 2001
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Optimized spatial hashing for collision detection of deformable objects
M. Teschner, B. Heidelberger, M. Müller, D. Pomeranets, and M. Gross · 2003
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Automatic photo pop-up
D. Hoiem, A. A. Efros, and M. Hebert · 2005
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Geometric context from a single image
D. Hoiem, A. A. Efros, and M. Hebert · 2005
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Learning depth from single monocular images
A. Saxena, S. H. Chung, and A. Y. Ng · 2006
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A comparison and evaluation of multi-view stereo reconstruction algorithms
S. M. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski · 2006
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Recovering surface layout from an image
D. Hoiem, A. A. Efros, and M. Hebert · 2007
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Putting objects in perspective
D. Hoiem, A. Efros, and M. Hebert · 2008
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3-D depth reconstruction from a single still image
A. Saxena, S. H. Chung, and A. Y. Ng · 2008
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Make3D: learning 3D scene structure from a single still image
A. Saxena, M. Sun, and A. Y. Ng · 2009
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Patchmatch stereo - stereo matching with slanted support windows
M. Bleyer, C. Rhemann, and C. Rother · 2011
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Screened poisson surface reconstruction
M. M. Kazhdan and H. Hoppe · 2013
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Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
D. Eigen and R. Fergus · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Towards probabilistic volumetric reconstruction using ray potentials
A. O. Ulusoy, A. Geiger, and M. J. Black · 2015
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Learning to compare image patches via convolutional neural networks
S. Zagoruyko and N. Komodakis · 2015
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Large-scale data for multiple-view stereopsis
H. Aanæs, R. R. Jensen, G. Vogiatzis, E. Tola, and A. B. Dahl · 2016
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Efficient deep learning for stereo matching
W. Luo, A. Schwing, and R. Urtasun · 2016
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Pixelwise view selection for unstructured multi-view stereo
J. L. Schönberger, E. Zheng, M. Pollefeys, and J.-M. Frahm · 2016
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Structure-from-motion revisited
J. L. Schönberger and J.-M. Frahm · 2016
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Niessner · 2017
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Learned multi-patch similarity
W. Hartmann, S. Galliani, M. Havlena, L. Van Gool, and K. Schindler · 2017
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Tanks and temples: Benchmarking large-scale scene reconstruction
A. Knapitsch, J. Park, Q.-Y. Zhou, and V. Koltun · 2017
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OctNetFusion: Learning depth fusion from data
G. Riegler, A. O. Ulusoy, H. Bischof, and A. Geiger · 2017
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Multi-view supervision for single-view reconstruction via differentiable ray consistency
S. Tulsiani, T. Zhou, A. A. Efros, and J. Malik · 2017
Cited alongside, same era.
Demon: Depth and motion network for learning monocular stereo
B. Ummenhofer, H. Zhou, J. Uhrig, N. Mayer, E. Ilg, A. Dosovitskiy, and T. Brox · 2017
Cited alongside, same era.
Deepmvs: Learning multi-view stereopsis
P. Huang, K. Matzen, J. Kopf, N. Ahuja, and J. Huang · 2018
Cited alongside, same era.
Shape reconstruction using volume sweeping and learned photoconsistency
V. Leroy, J. Franco, and E. Boyer · 2018
Cited alongside, same era.
Raynet: Learning volumetric 3d reconstruction with ray potentials
D. Paschalidou, A. O. Ulusoy, C. Schmitt, L. van Gool, and A. Geiger · 2018
Cited alongside, same era.
Mvsnet: Depth inference for unstructured multi-view stereo
Y. Yao, Z. Luo, S. Li, T. Fang, and L. Quan · 2018
Visibility-aware multi-view stereo network
J. Zhang, Y. Yao, S. Li, Z. Luo, and T. Fang · 2020
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Nerf++: Analyzing and improving neural radiance fields
K. Zhang, G. Riegler, N. Snavely, and V. Koltun · 2020
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Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans
A. Eftekhar, A. Sax, J. Malik, and A. Zamir · 2021
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Neural radiosity
S. Hadadan, S. Chen, and M. Zwicker · 2021
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Di-fusion: Online implicit 3d reconstruction with deep priors
J. Huang, S.-S. Huang, H. Song, and S.-M. Hu · 2021
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Putting nerf on a diet: Semantically consistent few-shot view synthesis
A. Jain, M. Tancik, and P. Abbeel · 2021
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Cited alongside, same era.
Learning implicit fields for generative shape modeling
Z. Chen and H. Zhang · 2019
Cited alongside, same era.
Learning non-volumetric depth fusion using successive reprojections
S. Donne and A. Geiger · 2019
Cited alongside, same era.
Occupancy networks: Learning 3d reconstruction in function space
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger · 2019
Cited alongside, same era.
Occupancy flow: 4d reconstruction by learning particle dynamics
M. Niemeyer, L. Mescheder, M. Oechsle, and A. Geiger · 2019
Cited alongside, same era.
Deepsdf: Learning continuous signed distance functions for shape representation
J. J. Park, P. Florence, J. Straub, R. A. Newcombe, and S. Lovegrove · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
Cited alongside, same era.
Later among the works it cites.
Structdepth: Leveraging the structural regularities for self-supervised indoor depth estimation
B. Li, Y. Huang, Z. Liu, D. Zou, and W. Yu · 2021
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Acorn: Adaptive coordinate networks for neural scene representation
J. N. Martel, D. B. Lindell, C. Z. Lin, E. R. Chan, M. Monteiro, and G. Wetzstein · 2021
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Boosting monocular depth estimation models to high-resolution via content-adaptive multi-resolution merging
S. M. H. Miangoleh, S. Dille, L. Mai, S. Paris, and Y. Aksoy · 2021
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Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction
M. Oechsle, S. Peng, and A. Geiger · 2021
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Shape as points: A differentiable poisson solver
S. Peng, C. M. Jiang, Y. Liao, M. Niemeyer, M. Pollefeys, and A. Geiger · 2021
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Vision transformers for dense prediction
R. Ranftl, A. Bochkovskiy, and V. Koltun · 2021
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Dense depth priors for neural radiance fields from sparse input views
B. Roessle, J. T. Barron, B. Mildenhall, P. P. Srinivasan, and M. Nießner · 2021
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iMAP: Implicit mapping and positioning in real-time
E. Sucar, S. Liu, J. Ortiz, and A. Davison · 2021
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Neural geometric level of detail: Real-time rendering with implicit 3D shapes
T. Takikawa, J. Litalien, K. Yin, K. Kreis, C. Loop, D. Nowrouzezahrai, A. Jacobson, M. McGuire, and S. Fidler · 2021
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Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction
P. Wang, L. Liu, Y. Liu, C. Theobalt, T. Komura, and W. Wang · 2021
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Metaavatar: Learning animatable clothed human models from few depth images
S. Wang, M. Mihajlovic, Q. Ma, A. Geiger, and S. Tang · 2021
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Volume rendering of neural implicit surfaces
L. Yariv, J. Gu, Y. Kasten, and Y. Lipman · 2021
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Learning to recover 3d scene shape from a single image
W. Yin, J. Zhang, O. Wang, S. Niklaus, L. Mai, S. Chen, and C. Shen · 2021
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pixelNeRF: Neural radiance fields from one or few images
A. Yu, V. Ye, M. Tancik, and A. Kanazawa · 2021
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NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination
X. Zhang, P. P. Srinivasan, B. Deng, P. Debevec, W. T. Freeman, and J. T. Barron · 2021
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Neural rgb-d surface reconstruction
D. Azinović, R. Martin-Brualla, D. B. Goldman, M. Nießner, and J. Thies · 2022
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Mip-nerf 360: Unbounded anti-aliased neural radiance fields
J. T. Barron, B. Mildenhall, D. Verbin, P. P. Srinivasan, and P. Hedman · 2022
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Depth-supervised nerf: Fewer views and faster training for free
K. Deng, A. Liu, J.-Y. Zhu, and D. Ramanan · 2022
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Neural 3d scene reconstruction with the manhattan-world assumption
H. Guo, S. Peng, H. Lin, Q. Wang, G. Zhang, H. Bao, and X. Zhou · 2022
Closest in time.
Instant neural graphics primitives with a multiresolution hash encoding
T. Müller, A. Evans, C. Schied, and A. Keller · 2022
Closest in time.
Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs
M. Niemeyer, J. T. Barron, B. Mildenhall, M. S. Sajjadi, A. Geiger, and N. Radwan · 2022
Closest in time.
Neuris: Neural reconstruction of indoor scenes using normal priors
J. Wang, P. Wang, X. Long, C. Theobalt, T. Komura, L. Liu, and W. Wang · 2022
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Neural fields in visual computing and beyond
Y. Xie, T. Takikawa, S. Saito, O. Litany, S. Yan, N. Khan, F. Tombari, J. Tompkin, V. Sitzmann, and S. Sridhar · 2022
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Nice-slam: Neural implicit scalable encoding for slam
Z. Zhu, S. Peng, V. Larsson, W. Xu, H. Bao, Z. Cui, M. R. Oswald, and M. Pollefeys · 2022
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