Recurrent Slice Networks for 3D Segmentation of Point Clouds

@article{Huang2018RecurrentSN,
  title={Recurrent Slice Networks for 3D Segmentation of Point Clouds},
  author={Qiangui Huang and Weiyue Wang and Ulrich Neumann},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2018},
  pages={2626-2635}
}
Point clouds are an efficient data format for 3D data. However, existing 3D segmentation methods for point clouds either do not model local dependencies [21] or require added computations [14, 23]. This work presents a novel 3D segmentation framework, RSNet1, to efficiently model local structures in point clouds. The key component of the RSNet is a lightweight local dependency module. It is a combination of a novel slice pooling layer, Recurrent Neural Network (RNN) layers, and a slice… 

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