The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
A Papier-Mache Approach to Learning 3D Surface Generation
Abstract
We introduce a method for learning to generate the surface of 3D shapes. Our approach represents a 3D shape as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers a surface representation of the shape. Beyond its novelty, our new shape generation framework, AtlasNet, comes with significant advantages, such as improved precision and generalization capabilities, and the possibility to generate a shape of arbitrary resolution without memory issues. We demonstrate these benefits and compare to strong baselines on the ShapeNet benchmark for two applications: (i) autoencoding shapes, and (ii) single-view reconstruction from a still image. We also provide results showing its potential for other applications, such as morphing, parametrization, super-resolution, matching, and co-segmentation.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
A method for registration of 3-D shapes
Besl, P. J., & McKay, N. D. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.121791 · Cited by 18,103
Approximation capabilities of multilayer feedforward networks
Hornik, K. (1991) · Neural Networks · doi:10.1016/0893-6080(91)90009-t · Cited by 6,091
A Point Set Generation Network for 3D Object Reconstruction from a Single Image
Fan, H., Su, H., & Guibas, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.264 · Cited by 2,402
Screened poisson surface reconstruction
Kazhdan, M., & Hoppe, H. (2013) · ACM Transactions on Graphics · doi:10.1145/2487228.2487237 · Cited by 2,231
Metro: Measuring Error on Simplified Surfaces
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O-CNN: octree-based convolutional neural networks for 3D shape analysis
Wang, P.-S., Liu, Y., Guo, Y.-X., Sun, C.-Y., & Tong, X. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073608 · Cited by 884
Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs
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Cited by
Gkioxari, G., Johnson, J., & Malik, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00988 · Cited by 441
ShellNet: Efficient Point Cloud Convolutional Neural Networks Using Concentric Shells Statistics
Zhang, Z., Hua, B.-S., & Yeung, S.-K. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00169 · Cited by 409
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
Chabra, R., Lenssen, J. E., Ilg, E., Schmidt, T., Straub, J., Lovegrove, S., & Newcombe, R. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58526-6_36 · Cited by 380
3D-CODED: 3D Correspondences by Deep Deformation
Groueix, T., Fisher, M., Kim, V. G., Russell, B. C., & Aubry, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01216-8_15 · Cited by 293
Self-citationStructured3D: A Large Photo-Realistic Dataset for Structured 3D Modeling
Zheng, J., Zhang, J., Li, J., Tang, R., Gao, S., & Zhou, Z. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58545-7_30 · Cited by 282
Metadata
- Authors
- Groueix, T., Fisher, M., Kim, V. G., Russell, B. C., & Aubry, M.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 216-224
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2885317998
- Cited by
- 910
- References
- 21
- FWCI
- 115.69
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Groueix, T., Fisher, M., Kim, V. G., Russell, B. C., & Aubry, M. (2018). A Papier-Mache Approach to Learning 3D Surface Generation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00030
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