The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction
Open accessAbstract
Conventional methods of 3D object generative modeling learn volumetric predictions using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones. However, these methods are computationally wasteful in attempt to predict 3D shapes, where information is rich only on the surfaces. In this paper, we propose a novel 3D generative modeling framework to efficiently generate object shapes in the form of dense point clouds. We use 2D convolutional operations to predict the 3D structure from multiple viewpoints and jointly apply geometric reasoning with 2D projection optimization. We introduce the pseudo-renderer, a differentiable module to approximate the true rendering operation, to synthesize novel depth maps for optimization. Experimental results for single-image 3D object reconstruction tasks show that we outperforms state-of-the-art methods in terms of shape similarity and prediction density.
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Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
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
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
Learning 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
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
Convolutional Occupancy Networks
Peng, S., Niemeyer, M., Mescheder, L., Pollefeys, M., & Geiger, A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58580-8_31 · Cited by 801
PU-Net: Point Cloud Upsampling Network
Yu, L., Li, X., Fu, C.-W., Cohen-Or, D., & Heng, P.-A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00295 · Cited by 697
PF-Net: Point Fractal Network for 3D Point Cloud Completion
Huang, Z., Yu, Y., Xu, J., Ni, F., & Le, X. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00768 · Cited by 513
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
Metadata
- Authors
- Lin, C.-H., Kong, C., & Lucey, S.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2018-04-27
- Volume
- 32
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2698857938
- Cited by
- 385
- References
- 4
- FWCI
- 72.07
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Lin, C.-H., Kong, C., & Lucey, S. (2018). Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v32i1.12278
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