The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs
Open accessAbstract
We present a deep convolutional decoder architecture that can generate volumetric 3D outputs in a compute- and memory-efficient manner by using an octree representation. The network learns to predict both the structure of the octree, and the occupancy values of individual cells. This makes it a particularly valuable technique for generating 3D shapes. In contrast to standard decoders acting on regular voxel grids, the architecture does not have cubic complexity. This allows representing much higher resolution outputs with a limited memory budget. We demonstrate this in several application domains, including 3D convolutional autoencoders, generation of objects and whole scenes from high-level representations, and shape from a single image.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945
Self-citationFlownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.316 · Cited by 4,313
Self-citationLearning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Wu, J., Zhang, C., Xue, T., Freeman, W. T., & Tenenbaum, J. B. (2016) · arXiv · doi:10.48550/arxiv.1610.07584 · Cited by 1,393 · cited 1×
“Applications include single- and multi-view 3D object reconstruction trained in supervised [14, 5, 7, 17] and unsupervised [37, 12, 19] ways, probabilistic generative modeling of 3D shapes [36, 35, 29], semantic segmentation [2, 4] and shape deformation [39].”
MethodGeodesic Convolutional Neural Networks on Riemannian Manifolds
Masci, J., Boscaini, D., Bronstein, M. M., & Vandergheynst, P. (2015) · 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2015.112 · Cited by 721
SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation
Yi, L., Su, H., Guo, X., & Guibas, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.697 · Cited by 461
Multi-view 3D Models from Single Images with a Convolutional Network
Tatarchenko, M., Dosovitskiy, A., & Brox, T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46478-7_20 · Cited by 337
Self-citationLearning Semantic Deformation Flows with 3D Convolutional Networks
Yumer, M. E., & Mitra, N. J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_18 · Cited by 60
Cited by
Dynamic Graph CNN for Learning on Point Clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M. (2019) · ACM Transactions on Graphics · doi:10.1145/3326362 · Cited by 7,199
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Park, J. J., Florence, P., Straub, J., Newcombe, R., & Lovegrove, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00025 · Cited by 3,422
Occupancy Networks: Learning 3D Reconstruction in Function Space
Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., & Geiger, A. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00459 · Cited by 2,708
Learning Implicit Fields for Generative Shape Modeling
Chen, Z., & Zhang, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00609 · Cited by 1,443
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., & Jiang, Y.-G. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01252-6_4 · Cited by 1,306
Metadata
- Authors
- Tatarchenko, M., Dosovitskiy, A., & Brox, T.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 2107-2115
- OpenAlex
- W2603429625
- Cited by
- 742
- References
- 9
- FWCI
- 141.27
- Type
- conference paper
- Language
- en
- Open access
- green
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- Tatarchenko, M., Dosovitskiy, A., & Brox, T. (2017). Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs. 2017 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2017.230
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