The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
SO-Net: Self-Organizing Network for Point Cloud Analysis
Open accessAbstract
This paper presents SO-Net, a permutation invariant architecture for deep learning with orderless point clouds. The SO-Net models the spatial distribution of point cloud by building a Self-Organizing Map (SOM). Based on the SOM, SO-Net performs hierarchical feature extraction on individual points and SOM nodes, and ultimately represents the input point cloud by a single feature vector. The receptive field of the network can be systematically adjusted by conducting point-to-node k nearest neighbor search. In recognition tasks such as point cloud reconstruction, classification, object part segmentation and shape retrieval, our proposed network demonstrates performance that is similar with or better than state-of-the-art approaches. In addition, the training speed is significantly faster than existing point cloud recognition networks because of the parallelizability and simplicity of the proposed architecture. Our code is available at the project website1.
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Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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Johnson, J., Douze, M., & Jegou, H. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2921572 · Cited by 4,228
Unsupervised Monocular Depth Estimation with Left-Right Consistency
Godard, C., Aodha, O. M., & Brostow, G. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.699 · Cited by 3,270
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
Garg, R., B.G., V. K., Carneiro, G., & Reid, I. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_45 · Cited by 1,632
Geodesic Convolutional Neural Networks on Riemannian Manifolds
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Voting for Voting in Online Point Cloud Object Detection
Zeng Wang, D., & Posner, I. (2015) · Robotics: Science and Systems XI · doi:10.15607/rss.2015.xi.035 · Cited by 337
Unsupervised Monocular Depth Estimation with Left-Right Consistency
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Cited by
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., & Markham, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01112 · Cited by 2,275
Feng, Y., You, H., Zhang, Z., Ji, R., & Gao, Y. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013558 · Cited by 1,895
Beyond Self-Attention: External Attention Using Two Linear Layers for Visual Tasks
Guo, M.-H., Liu, Z.-N., Mu, T.-J., & Hu, S.-M. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3211006 · Cited by 668
Mo, K., Zhu, S., Chang, A. X., Yi, L., Tripathi, S., Guibas, L. J., & Su, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00100 · Cited by 667
Masked Autoencoders for Point Cloud Self-supervised Learning
Pang, Y., Wang, W., Tay, F. E. H., Liu, W., Tian, Y., & Yuan, L. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20086-1_35 · Cited by 605
Metadata
- Authors
- Li, J., Chen, B. M., & Lee, G. H.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 9397-9406
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963719584
- Cited by
- 985
- References
- 8
- FWCI
- 181.11
- Type
- conference paper
- Language
- en
- Open access
- green
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- Li, J., Chen, B. M., & Lee, G. H. (2018). SO-Net: Self-Organizing Network for Point Cloud Analysis. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00979
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