Corpus ID: 153312807

LSANet: Feature Learning on Point Sets by Local Spatial Attention

@article{Chen2019LSANetFL,
  title={LSANet: Feature Learning on Point Sets by Local Spatial Attention},
  author={Lin-Zhuo Chen and Xuan-yi Li and Deng-Ping Fan and Ming-Ming Cheng and K. Wang and Shao-Ping Lu},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.05442}
}
  • Lin-Zhuo Chen, Xuan-yi Li, +3 authors Shao-Ping Lu
  • Published 2019
  • Computer Science
  • ArXiv
  • Directly learning features from the point cloud has become an active research direction in 3D understanding. Existing learning-based methods usually construct local regions from the point cloud and extract the corresponding features using shared Multi-Layer Perceptron (MLP) and max pooling. However, most of these processes do not adequately take the spatial distribution of the point cloud into account, limiting the ability to perceive fine-grained patterns. We design a novel Local Spatial… CONTINUE READING
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    References

    SHOWING 1-10 OF 36 REFERENCES
    Local Spectral Graph Convolution for Point Set Feature Learning
    • 103
    • PDF
    PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
    • 1,898
    • Highly Influential
    • PDF
    PointCNN: Convolution On X-Transformed Points
    • 371
    • Highly Influential
    • PDF
    PointCNN: convolution on Χ -transformed points
    • 299
    • Highly Influential
    Attentional ShapeContextNet for Point Cloud Recognition
    • 86
    • PDF
    SPLATNet: Sparse Lattice Networks for Point Cloud Processing
    • Hang Su, V. Jampani, +4 authors J. Kautz
    • Computer Science
    • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
    • 2018
    • 295
    • PDF
    PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
    • 3,047
    • Highly Influential
    • PDF
    Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks
    • 252
    • Highly Influential
    • PDF
    Recurrent Slice Networks for 3D Segmentation of Point Clouds
    • Qiangui Huang, Weiyue Wang, U. Neumann
    • Computer Science
    • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
    • 2018
    • 162
    • PDF
    SO-Net: Self-Organizing Network for Point Cloud Analysis
    • 313
    • Highly Influential
    • PDF