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Reconstructing continuous surfaces from 3D point clouds is a fundamental operation in 3D geometry processing.
Marching cubes: A high resolution 3d surface construction algorithm
Lorensen, W. E. and Cline, H. E · 1987
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The ball-pivoting algorithm for surface reconstruction
Bernardini, F., Mittleman, J., Rushmeier, H., Silva, C., and Taubin, G · 1999
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SALD: Sign agnostic learning with derivatives
Atzmon, M. and Lipman, Y · 2006
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Neural unsigned distance fields for implicit function learning
Chibane, J., Mir, A., and Pons-Moll, G · 2010
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Screened poisson surface reconstruction
Kazhdan, M. M. and Hoppe, H · 2013
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ShapeNet: An Information-Rich 3D Model Repository
Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., and Yu, F · 2015
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3D-R2N2: A unified approach for single and multi-view 3D object reconstruction
Choy, C. B., Xu, D., Gwak, J., Chen, K., and Savarese, S · 2016
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A point set generation network for 3D object reconstruction from a single image
Fan, H., Su, H., and Guibas, L. J · 2017
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A papier-mâché approach to learning 3D surface generation
Groueix, T., Fisher, M., Kim, V. G., Russell, B. C., and Aubry, M · 2018
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Deep marching cubes: Learning explicit surface representations
Liao, Y., Donné, S., and Geiger, A · 2018
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Pixel2mesh: Generating 3D mesh models from single RGB images
Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., and Jiang, Y · 2018
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Learning implicit fields for generative shape modeling
Chen, Z. and Zhang, H · 2019
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Learning shape templates with structured implicit functions
Genova, K., Cole, F., Vlasic, D., Sarna, A., Freeman, W. T., and Funkhouser, T · 2019
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ABC: A big cad model dataset for geometric deep learning
Koch, S., Matveev, A., Jiang, Z., Williams, F., Artemov, A., Burnaev, E., Alexa, M., Zorin, D., and Panozzo, D · 2019
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Deep meta functionals for shape representation
Littwin, G. and Wolf, L · 2019
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Occupancy networks: Learning 3D reconstruction in function space
Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., and Geiger, A · 2019
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Deep level sets: Implicit surface representations for 3D shape inference
Michalkiewicz, M., Pontes, J. K., Jack, D., Baktashmotlagh, M., and Eriksson, A. P · 2019
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DeepSDF: Learning continuous signed distance functions for shape representation
Park, J. J., Florence, P., Straub, J., Newcombe, R., and Lovegrove, S · 2019
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PIFu: Pixel-aligned implicit function for high-resolution clothed human digitization
Saito, S., , Huang, Z., Natsume, R., Morishima, S., Kanazawa, A., and Li, H · 2019
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Scene representation networks: Continuous 3D-structure-aware neural scene representations
Sitzmann, V., Zollhöfer, M., and Wetzstein, G · 2019
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What do single-view 3D reconstruction networks learn?
Tatarchenko, M., Richter, S. R., Ranftl, R., Li, Z., Koltun, V., and Brox, T · 2019
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Deep geometric prior for surface reconstruction
Williams, F., Schneider, T., Silva, C., Zorin, D., Bruna, J., and Panozzo, D · 2019
Cited alongside, same era.
Meshlet priors for 3D mesh reconstruction
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 · 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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Neural scene graphs for dynamic scenes
Ost, J., Mannan, F., Thuerey, N., Knodt, J., and Heide, F · 2020
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Implicit neural representations with periodic activation functions
Sitzmann, V., Martel, J. N., Bergman, A. W., Lindell, D. B., and Wetzstein, G · 2020
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Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, L. M. M. P. A. G · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
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Badki, A., Gallo, O., Kautz, J., and Sen, P · 2020
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Shape reconstruction by learning differentiable surface representations
Bednarik, J., Parashar, S., Gundogdu, E., and Salzmann, Mathieu andFua, P · 2020
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Deep local shapes: Learning local SDF priors for detailed 3D reconstruction
Chabra, R., Lenssen, J. E., Ilg, E., Schmidt, T., Straub, J., Lovegrove, S., and Newcombe, R. A · 2020
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Points2Surf: Learning implicit surfaces from point clouds
Erler, P., Guerrero, P., Ohrhallinger, S., Mitra, N. J., and Wimmer, M · 2020
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Local deep implicit functions for 3d shape
Genova, K., Cole, F., Sud, A., Sarna, A., and Funkhouser, T · 2020
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Implicit geometric regularization for learning shapes
Gropp, A., Yariv, L., Haim, N., Atzmon, M., and Lipman, Y · 2020
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3D shape completion with multi-view consistent inference
Hu, T., Han, Z., and Zwicker, M · 2020
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Tancik, M., Srinivasan, P. P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J. T., and Ng, R · 2020
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PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations
Tretschk, E., Tewari, A., Golyanik, V., Zollhöfer, M., Stoll, C., and Theobalt, C · 2020
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DFR: differentiable function rendering for learning 3D generation from images
Wu, Y. and Sun, Z · 2020
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Autolabeling 3D objects with differentiable rendering of sdf shape priors
Zakharov, S., Kehl, W., Bhargava, A., and Gaidon, A · 2020
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Neural RGB-D surface reconstruction
Azinovic, D., Martin-Brualla, R., Goldman, D. B., Nießner, M., and Thies, J · 2021
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Generative models as distributions of functions
Dupont, E., Teh, Y. W., and Doucet, A · 2021
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Fine-grained 3d shape classification with hierarchical part-view attention
Liu, X., Han, Z., Liu, Y.-S., and Zwicker, M · 2021
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ACORN: adaptive coordinate networks for neural scene representation
Martel, J. N. P., Lindell, D. B., Lin, C. Z., Chan, E. R., Monteiro, M., and Wetzstein, G · 2021
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UNISURF: unifying neural implicit surfaces and radiance fields for multi-view reconstruction
Oechsle, M., Peng, S., and Geiger, A · 2021
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Sharf: Shape-conditioned radiance fields from a single view
Rematas, K., Martin-Brualla, R., and Ferrari, V · 2021
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Neural geometric level of detail: Real-time rendering with implicit 3D shapes
Takikawa, T., Litalien, J., Yin, K., Kreis, K., Loop, C., Nowrouzezahrai, D., Jacobson, A., McGuire, M., and Fidler, S · 2021
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