The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Open accessAbstract
Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure. Among existing works, PointNet has achieved promising results by directly learning on point sets. However, it does not take full advantage of a point's local neighborhood that contains fine-grained structural information which turns out to be helpful towards better semantic learning. In this regard, we present two new operations to improve PointNet with a more efficient exploitation of local structures. The first one focuses on local 3D geometric structures. In analogy to a convolution kernel for images, we define a point-set kernel as a set of learnable 3D points that jointly respond to a set of neighboring data points according to their geometric affinities measured by kernel correlation, adapted from a similar technique for point cloud registration. The second one exploits local high-dimensional feature structures by recursive feature aggregation on a nearest-neighbor-graph computed from 3D positions. Experiments show that our network can efficiently capture local information and robustly achieve better performances on major datasets. Our code is available at http://www.merl.com/research/license#KCNet.
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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
KPConv: Flexible and Deformable Convolution for Point Clouds
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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
FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation
Yang, Y., Feng, C., Shen, Y., & Tian, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00029 · Cited by 1,313
Self-citationPAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds
Xu, M., Ding, R., Zhao, H., & Qi, X. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00319 · Cited by 523
Metadata
- Authors
- Shen, Y., Feng, C., Yang, Y., & Tian, D.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 4548-4557
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963053547
- Cited by
- 532
- References
- 39
- FWCI
- 107.93
- Type
- conference paper
- Language
- en
- Open access
- green
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