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PointNGCNN: Deep convolutional networks on 3D point clouds with neighborhood graph filters

Installation

Install Tensorflow. You may also need to install h5py. The code has been tested with python 3.5, tensorflow 1.7, CUDA 9.0 and cuDNN 7.0 on Ubuntu 16.04.

Usage

We used the same data in this paper as pointnet++. Please go to data folder, follow instructions there and download the data. You are recommended to put unziped modelnet and shapenet folders in data/, but they can of course go somewhere else.

- Shape Classification

  • Run the training script:
    python train.py
  • Run the evaluation script after training finished:
    python evalutate.py

- Part Segmentation

Follow ModelNet instructions, but apply those in folder "part_seg", and you should be able to run the experiments smoothly.

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