megayeye / triangle-net

(paper code) Triangle-Net: Towards Robustness in Point Cloud Learning

Classification

1. Prepare data

   For ModelNet 40, download dataset from https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip

   Next, extract it to <code folder>/data/modelnet40_ply_hdf5_2048 folder.

   For ScanObjectNN, please get the download link according to the instruction from this link (https://hkust-vgd.github.io/scanobjectnn/). Then, extract training_objectdataset_augmentedrot_scale75.h5 and test_objectdataset_augmentedrot_scale75.h5 to <code folder>/data/ScanObjectNN_nobg.

2. training

   for training on ModelNet40 with reconstruction network:

   python train_recon.py
   

   for training on ModelNet40 without reconstruction network, of which the training is faster at a cost of minor accuracy drop:

   python train_wo_recon.py
   

   For both of the training configuration, --n_points can specify the number of points.

   For training on ScanObjectNN:

   python train_scanobjects.py
   

Segmentation

1. Prepare data

   Download dataset from: https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip

   Next, extract it to <code folder>/data/shapenetcore_partanno_segmentation_benchmark_v0_normal folder

2. Preprocessing

   To accelerate disk IO, we save the dataset as npy files:

   python segment_data_preprocess.py
   

3. Training

   python train_partseg.py
   

Comparison experiment

We refer the following code for comparison experiments

[PointNet & PointNet++] https://github.com/yanx27/Pointnet_Pointnet2_pytorch

[DGCNN] https://github.com/WangYueFt/dgcnn

[RI-CONV] https://github.com/hkust-vgd/riconv

[3DmFV] https://github.com/sitzikbs/3DmFV-Net

triangle-net's Issues

This is a query regarding Hypergraph

Hello sir,

I had a query regarding, what part of your code contributes for hypergraph construction and convolution, which is mentioned in paper.

It would be really great full if you could provide more insights

Thanks and regards.