Pointwise Convolutional Neural Networks

Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently. However, the capability of using point clouds with convolutional neural network has been so far not fully explored. In this paper, we present a convolutional neural network for semantic segmentation and object recognition with 3D point clouds. At the core of our network is point-wise convolution, a new convolution operator that can be applied at each point of a point cloud. Our fully convolutional network design, while being surprisingly simple to implement, can yield competitive accuracy in both semantic segmentation and object recognition task.

• Publication year

  2017

• Venue

  2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

• Publication date

  2017-12-14

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/CVPR.2018.00109arXiv 1712.05245
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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