The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Adaptive O-CNN: a patch-based deep representation of 3D shapes
Open accessAbstract
We present an Adaptive Octree-based Convolutional Neural Network (Adaptive O-CNN) for efficient 3D shape encoding and decoding. Different from volumetric-based or octree-based CNN methods that represent a 3D shape with voxels in the same resolution, our method represents a 3D shape adaptively with octants at different levels and models the 3D shape within each octant with a planar patch. Based on this adaptive patch-based representation, we propose an Adaptive O-CNN encoder and decoder for encoding and decoding 3D shapes. The Adaptive O-CNN encoder takes the planar patch normal and displacement as input and performs 3D convolutions only at the octants at each level, while the Adaptive O-CNN decoder infers the shape occupancy and subdivision status of octants at each level and estimates the best plane normal and displacement for each leaf octant. As a general framework for 3D shape analysis and generation, the Adaptive O-CNN not only reduces the memory and computational cost, but also offers better shape generation capability than the existing 3D-CNN approaches. We validate Adaptive O-CNN in terms of efficiency and effectiveness on different shape analysis and generation tasks, including shape classification, 3D autoencoding, shape prediction from a single image, and shape completion for noisy and incomplete point clouds.
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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
ImageNet: A large-scale hierarchical image database
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Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Geometric Deep Learning: Going beyond Euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661
Learning 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 4×
“The commonly used (dense)-voxel representation is most popular in existing 3D learning and generation frameworks [Wu et al. 2015] [Wu et al. 2016] since it is a natural extension to 2D images and is well-suited to existing learning frameworks, like convolutional neural networks.”
BackgroundMethodInfluentialGeodesic 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
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
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
Tang, H., Liu, Z., Zhao, S., Lin, Y., Lin, J., Wang, H., & Han, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58604-1_41 · Cited by 733
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
Patch-Based Progressive 3D Point Set Upsampling
Yifan, W., Wu, S., Huang, H., Cohen-Or, D., & Sorkine-Hornung, O. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00611 · Cited by 317
Metadata
- Authors
- Wang, P.-S., Sun, C.-Y., Liu, Y., & Tong, X.
- Venue
- ACM Transactions on Graphics
- Published
- 2018-12-04
- Volume
- 37
- Issue
- 6
- Pages
- 1-11
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2890556874
- Cited by
- 193
- References
- 6
- FWCI
- 23.39
- Type
- journal article
- Language
- en
- Open access
- green
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