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Deep implicit surfaces excel at modeling generic shapes but do not always capture the regularities present in manufactured objects, which is something simple geometric primitives are particularly good at.
Topology Adaptive Deformable Surfaces for Medical Image Volume Segmentation
T. Mcinerney and D. Terzopoulos · 1999
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Level Set Methods and Fast Marching Methods Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science
J. A. Sethian · 1999
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Subdivision Surfaces
J. Peters and U. Reif · 2008
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Shapenet: An Information-Rich 3D Model Repository
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 · 2015
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Adam: A Method for Stochastic Optimization
D. P. Kingma and J. Ba · 2015
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3DR2N2: A Unified Approach for Single and Multi-View 3D Object Reconstruction
C. Choy, D. Xu, J. Gwak, K. Chen, and S. Savarese · 2016
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Fusionnet: 3D Object Classification Using Multiple Data Representations
V. Hegde and R. Zadeh · 2016
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Continuous Signed Distance Functions for 3D Vision
S. Haugo, A. Stahl, and E. Brekke · 2017
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3D Shape Segmentation with Projective Convolutional Networks
E. Kalogerakis, M. Averkiou, S. Maji, and S. Chaudhuri · 2017
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Imposing Hard Constraints on Deep Networks: Promises and Limitations
P. Marquez-Neila, M. Salzmann, and P. Fua · 2017
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Pointnet: Deep Learning on Point Sets for 3D Classification and Segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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Learning Shape Abstractions by Assembling Volumetric Primitives
S. Tulsiani, H. Su, L. J. Guibas, A. A. Efros, and J. Malik · 2017
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3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks
C. Zou, E. Yumer, J. Yang, D. Ceylan, and D. Hoiem · 2017
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Geodesic Convolutional Shape Optimization
P. Baqué, E. Remelli, F. Fleuret, and P. Fua · 2018
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Im2struct: Recovering 3D Shape Structure from a Single Rgb Image
C. Niu, J. Li, and K. Xu · 2018
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Learning Implicit Fields for Generative Shape Modeling
Z. Chen and H. Zhang · 2019
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Learning Shape Templates with Structured Implicit Functions
K. Genova, F. Cole, D. Vlasic, A. Sarna, W. T. Freeman, and T. Funkhouser · 2019
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Occupancy Networks: Learning 3D Reconstruction in Function Space
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger · 2019
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Shape Unicode: A Unified Shape Representation
S. Muralikrishnan, V. G. Kim, M. Fisher, and S. Chaudhuri · 2019
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DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
J. J. Park, P. Florence, J. Straub, R. A. Newcombe, and S. Lovegrove · 2019
Dualsdf: Semantic Shape Manipulation Using a Two-Level Representation
Z. Hao, H. Averbuch-Elor, N. Snavely, and S. Belongie · 2020
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Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single Rgb Image
D. Paschalidou, L. V. Gool, and A. Geiger · 2020
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Convolutional Occupancy Networks
S. Peng, M. Niemeyer, L. Mescheder, M. Pollefeys, and A. Geiger · 2020
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Meshsdf: Differentiable Iso-Surface Extraction
E. Remelli, A. Lukoianov, S. Richter, B. Guillard, T. Bagautdinov, P. Baque, and P. Fua · 2020
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Deep Parametric Shape Predictions Using Distance Fields
D. Smirnov, M. Fisher, V. G. Kim, R. Zhang, and J. Solomon · 2020
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Differentiable 3D CAD Programs for Bidirectional Editing
Dan Cascaval, Mira Shalah, Phillip Quinn, Rastislav Bodik, Maneesh Agrawala, and Adriana Schulz · 2021
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Cited alongside, same era.
Superquadrics Revisited: Learning 3D Shape Parsing Beyond Cuboids
D. Paschalidou, A. O. Ulusoy, and A. Geiger · 2019
Cited alongside, same era.
Learning Adaptive Hierarchical Cuboid Abstractions of 3D Shape Collections
C. Sun, Q. Zou, X. Tong, and Y. Liu · 2019
Cited alongside, same era.
DISN: Deep Implicit Surface Network for High-Quality Single-View 3D Reconstruction
Q. Xu, W. Wang, D. Ceylan, R. Mech, and U. Neumann · 2019
Cited alongside, same era.
Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion
J. Chibane, T. Alldieck, and G. Pons-Moll · 2020
Cited alongside, same era.
Cvxnet: Learnable Convex Decomposition
B. Deng, K. Genova, S. Yazdani, S. Bouaziz, G. Hinton, and A. Tagliasacchi · 2020
Cited alongside, same era.
Learning Generative Models of Shape Handles
M. Gadelha, G. Gori, D. Ceylan, R. Mech, N. Carr, T. Boubekeur, R. Wang, and S. Maji · 2020
Cited alongside, same era.
Closest in time.
Deepmesh: Differentiable Iso-Surface Extraction
B. Guillard, E. Remelli, A. Lukoianov, S. Richter, T. Bagautdinov, P. Baque, and P. Fua · 2021
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Sketch2mesh: Reconstructing and Editing 3D Shapes from Sketches
B. Guillard, E. Remelli, P. Yvernay, and P. Fua · 2021
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3D Shape Generation with Grid-Based Implicit Functions
M. Ibing, I. Lim, and L. Kobbelt · 2021
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Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images
F. Kluger, H. Ackermann, E. Brachmann, M. Yang, and B. Rosenhahn · 2021
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CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Clouds
Eric-Tuan Lê, Minhyuk Sung, Duygu Ceylan, Radomir Mech, Tamy Boubekeur, and Niloy J Mitra · 2021
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Deep Implicit Moving Least-Squares Functions for 3D Reconstruction
S. Liu, H. Guo, H. Pan, P. Wang, X. Tong, and Y. Liu · 2021
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Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks
D. Paschalidou, A. Katharopoulos, A. Geiger, and S. Fidler · 2021
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