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Geometric Deep Learning has recently made striking progress with the advent of continuous deep implicit fields.
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J. A. Sethian, Level Set Methods and Fast Marching Methods Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science . Cambridge University Press, 1999
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G. Allaire, F. Jouve, and A.-M. Toader, “A Level-Set Method for Shape Optimization,” Comptes Rendus Mathématiques , vol. 334, no. 12, pp. 1125–1130, 2002
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S. Osher and N. Paragios, Geometric Level Set Methods in Imaging, Vision, and Graphics . Springer, 2003
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Y. D. Fougerolle, A. Gribok, S. Foufou, F. Truchetet, and M. A. Abidi, “Boolean operations with implicit and parametric representation of primitives using r-functions,” IEEE Transactions on Visualization and Computer Graphics , 2005
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2011
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D. Toal and A. Keane, “Efficient Multipoint Aerodynamic Design Optimization via Cokriging,” Journal of Aircraft , vol. 48, no. 5, pp. 1685–1695, 2011
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F. S. Sin, D. Schroeder, and J. Barbič, “Vega: Non-Linear FEM Deformable Object Simulator,” in Computer Graphics Forum , 2013
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A. Chang, T. Funkhouser, L. G., P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu, “Shapenet: An Information-Rich 3D Model Repository,” in arXiv Preprint , 2015
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D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” in International Conference on Learning Representations , 2015
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M. Pharr, W. Jakob, and G. Humphreys, Physically Based Rendering: from Theory to Implementation . Morgan Kaufmann, 2016
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A. Brock, T. Lim, J. M. Ritchie, and N. Weston, “Generative and Discriminative Voxel Modeling with Convolutional Neural Networks,” in Advances in Neural Information Processing Systems , 2016
2016
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C. Choy, D. Xu, J. Gwak, K. Chen, and S. Savarese, “3D-R2n2: A Unified Approach for Single and Multi-View 3D Object Reconstruction,” in European Conference on Computer Vision , 2016, pp. 628–644
2016
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M. Gadelha, S. Maji, and R. Wang, “3D Shape Induction from 2D Views of Multiple Objects,” in arXiv Preprint , 2016
2016
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D. Rezende, S. Eslami, S. Mohamed, P. Battaglia, M. Jaderberg, and N. Heess, “Unsupervised Learning of 3D Structure from Images,” in Advances in Neural Information Processing Systems , 2016, pp. 4996–5004
2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Conference on Computer Vision and Pattern Recognition , 2016, pp. 770–778
2016
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D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Instance Normalization: The Missing Ingredient for Fast Stylization,” in arXiv Preprint , 2016
2016
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G. Riegler, A. O. Ulusoy, and A. Geiger, “Octnet: Learning Deep 3D Representations at High Resolutions,” in Conference on Computer Vision and Pattern Recognition , 2017
2017
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M. Tatarchenko, A. Dosovitskiy, and T. Brox, “Octree Generating Networks: Efficient Convolutional Architectures for High-Resolution 3D Outputs,” in International Conference on Computer Vision , 2017
2017
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E. Remelli, A. Tkach, A. Tagliasacchi, and M. Pauly, “Low-Dimensionality Calibration through Local Anisotropic Scaling for Robust Hand Model Personalization,” in International Conference on Computer Vision , 2017
2017
Cited alongside, same era.
H. Fan, H. Su, and L. Guibas, “A Point Set Generation Network for 3D Object Reconstruction from a Single Image,” in Conference on Computer Vision and Pattern Recognition , 2017
2017
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G. Xu, X. Liang, S. Yao, D. Chen, and Z. Li, “Multi-Objective Aerodynamic Optimization of the Streamlined Shape of High-Speed Trains Based on the Kriging Model,” PloS one , vol. 12, no. 1, pp. 1–14, 01 2017
2017
Cited alongside, same era.
M. Michalkiewicz, J. Pontes, D. Jack, M. Baktashmotlagh, and A. Eriksson, “Implicit Surface Representations as Layers in Neural Networks,” in International Conference on Computer Vision , 2019
2019
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H. Xie, H. Yao, X. Sun, S. Zhou, and S. Zhang, “Pix2vox: Context-Aware 3D Reconstruction from Single and Multi-View Images,” in Conference on Computer Vision and Pattern Recognition , 2019
2019
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M. Tatarchenko, S. Richter, R. Ranftl, Z. Li, V. Koltun, and T. Brox, “What Do Single-View 3D Reconstruction Networks Learn?” in Conference on Computer Vision and Pattern Recognition , 2019, pp. 3405–3414
2019
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Q. Xu, W. Wang, D. Ceylan, R. Mech, and U. Neumann, “DISN: Deep Implicit Surface Network for High-Quality Single-View 3D Reconstruction,” in Advances in Neural Information Processing Systems , 2019
2019
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F. Monti, D. Boscaini, J. Masci, E. Rodolà, J. Svoboda, and M. M. Bronstein, “Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs,” in Conference on Computer Vision and Pattern Recognition , 2017, pp. 5425–5434
2017
Cited alongside, same era.
P. Baqué, E. Remelli, F. Fleuret, and P. Fua, “Geodesic Convolutional Shape Optimization,” in International Conference on Machine Learning , 2018
2018
Cited alongside, same era.
N. Umetani and B. Bickel, “Learning Three-Dimensional Flow for Interactive Aerodynamic Design,” ACM Transactions on Graphics , vol. 37, no. 4, p. 89, 2018
2018
Cited alongside, same era.
Y. Liao, S. Donné, and A. Geiger, “Deep Marching Cubes: Learning Explicit Surface Representations,” in Conference on Computer Vision and Pattern Recognition , 2018, pp. 2916–2925
2018
Cited alongside, same era.
H. Kato, Y. Ushiku, and T. Harada, “Neural 3D Mesh Renderer,” in Conference on Computer Vision and Pattern Recognition , 2018
2018
Cited alongside, same era.
S. Richter and S. Roth, “Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers,” in Conference on Computer Vision and Pattern Recognition , 2018
2018
Cited alongside, same era.
A. Kanazawa, S. Tulsiani, A. Efros, and J. Malik, “Learning Category-Specific Mesh Reconstruction from Image Collections,” in Conference on Computer Vision and Pattern Recognition , 2018
2018
Cited alongside, same era.
T. Bagautdinov, C. Wu, J. Saragih, P. Fua, and Y. Sheikh, “Modeling Facial Geometry Using Compositional VAEs,” in Conference on Computer Vision and Pattern Recognition , 2018
2018
Cited alongside, same era.
K. Park, T. Patten, and M. Vincze, “Pix2pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation,” in International Conference on Computer Vision , 2019
2019
Later among the works it cites.
G. Gkioxari, J. Malik, and J. Johnson, “Mesh R-CNN,” in International Conference on Computer Vision , 2019
2019
Later among the works it cites.
S. Cheng, M. Bronstein, Y. Zhou, I. Kotsia, M. Pantic, and S. Zafeiriou, “Meshgan: Non-Linear 3D Morphable Models of Faces,” in arXiv Preprint , 2019
2019
Later among the works it cites.
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove, “Deepsdf: Learning Continuous Signed Distance Functions for Shape Representation,” in Conference on Computer Vision and Pattern Recognition , 2019
2019
Later among the works it cites.
S. Peng, M. Niemeyer, L. Mescheder, M. Pollefeys, and A. Geiger, “Convolutional Occupancy Networks,” in European Conference on Computer Vision , 2020, pp. 523–540
2020
Later among the works it cites.
U. Wickramasinghe, E. Remelli, G. Knott, and P. Fua, “Voxel2mesh: 3D Mesh Model Generation from Volumetric Data,” in Conference on Medical Image Computing and Computer Assisted Intervention , 2020
2020
Later among the works it cites.
Y. Jiang, D. Ji, Z. Han, and M. Zwicker, “Sdfdiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization,” in Conference on Computer Vision and Pattern Recognition , 2020
2020
Later among the works it cites.
E. Remelli, A. Lukoianov, S. Richter, B. Guillard, T. Bagautdinov, P. Baque, and P. Fua, “Meshsdf: Differentiable Iso-Surface Extraction,” in Advances in Neural Information Processing Systems , 2020
2020
Later among the works it cites.
J. Chibane, T. Alldieck, and G. Pons-Moll, “Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion,” in Conference on Computer Vision and Pattern Recognition , 2020
2020
Later among the works it cites.
S. Peng, W. Jiang, H. Pi, X. Li, H. Bao, and X. Zhou, “Deep Snake for Real-Time Instance Segmentation,” in Conference on Computer Vision and Pattern Recognition , 2020
2020
Later among the works it cites.
M. Niemeyer, L. Mescheder, M. Oechsle, and A. Geiger, “Differentiable Volumetric Rendering: Learning Implicit 3D Representations Without 3d Supervision,” in Conference on Computer Vision and Pattern Recognition , 2020
2020
Later among the works it cites.
L. Yariv, Y. Kasten, D. Moran, M. Galun, M. Atzmon, B. Ronen, and Y. Lipman, “Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance,” in Advances in Neural Information Processing Systems , 2020
2020
Later among the works it cites.
S. Liu, Y. Zhang, S. Peng, B. Shi, M. Pollefeys, and Z. Cui, “Dist: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracing,” in Conference on Computer Vision and Pattern Recognition , 2020
2020
Later among the works it cites.
P. Henderson and V. Ferrari, “Learning Single-Image 3D Reconstruction by Generative Modelling of Shape, Pose and Shading,” International Journal of Computer Vision , vol. 128, no. 4, pp. 835–854, 2020
2020
Later among the works it cites.
T. Yatagawa, “mcubes_pytorch: PyTorch Implementation for Marching Cubes,” 2020. [Online]. Available: https://github.com/tatsy/mcubes_pytorch/
2020
Later among the works it cites.
M. Atzmon and Y. Lipman, “SAL: Sign Agnostic Learning of Shapes from Raw Data,” in Conference on Computer Vision and Pattern Recognition , 2020
2020
Later among the works it cites.
R. Venkatesh, T. Karmali, S. Sharma, A. Ghosh, R. V. Babu, L. A. Jeni, and M. Singh, “Deep Implicit Surface Point Prediction Networks,” in International Conference on Computer Vision , 2021
2021
Closest in time.
B. Guillard, E. Remelli, P. Yvernay, and P. Fua, “Sketch2mesh: Reconstructing and Editing 3D Shapes from Sketches,” in International Conference on Computer Vision , 2021
2021
Closest in time.
D. Oner, M. Koziński, L. Citraro, N. C. Dadap, A. G. Konings, and P. Fua, “Promoting Connectivity of Network-Like Structures by Enforcing Region Separation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021
2021
Closest in time.