Corpus ID: 236447840

CKConv: Learning Feature Voxelization for Point Cloud Analysis

@article{Woo2021CKConvLF,
  title={CKConv: Learning Feature Voxelization for Point Cloud Analysis},
  author={Sungmin Woo and Dogyoon Lee and Junhyeop Lee and Sangwon Hwang and Woojin Kim and Sangyoun Lee},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.12655}
}
  • Sungmin Woo, Dogyoon Lee, +3 authors Sangyoun Lee
  • Published 2021
  • Computer Science
  • ArXiv
Despite the remarkable success of deep learning, optimal convolution operation on point cloud remains indefinite due to its irregular data structure. In this paper, we present Cubic Kernel Convolution (CKConv) that learns to voxelize the features of local points by exploiting both continuous and discrete convolutions. Our continuous convolution uniquely employs a 3D cubic form of kernel weight representation that splits a feature into voxels in embedding space. By consecutively applying… Expand

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References

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The dynamic filter is extended to a new convolution operation, named PointConv, which can be applied on point clouds to build deep convolutional networks and is able to achieve state-of-the-art on challenging semantic segmentation benchmarks on 3D point clouds. Expand
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