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leonardozcm / ASFM-Net-Pytorch

This is a unofficial implements of ASFM-Net, hope it works...
MIT License
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ASFM-Net-Pytorch

Status: Done

This is an unofficial implement of ASFM-Net, and I have achieved CD-P 11.87 at PCN datasets.🚀

Official repo: https://github.com/Yan-Xia/ASFM-Net

Preparation

pytorch > 1.10.1 required!

  1. Complie Chamfer3D, code from https://github.com/AllenXiangX/SnowflakeNet 
    cd Chamfer3D
python setup.py --user install
  2. Complie pointnet++ which includes fps_sampling we need, code from https://github.com/AllenXiangX/SnowflakeNet 
    cd pointnet2_ops_lib
python setup.py --user install
  3. Dataset path configs at config_pcn.py, modify it refer to https://github.com/hzxie/GRNet 
    __C.DATASETS.SHAPENET.PARTIAL_POINTS_PATH = '/home/chriskafka/dataset/ShapeNetCompletion/%s/partial/%s/%s/%02d.pcd'
__C.DATASETS.SHAPENET.COMPLETE_POINTS_PATH = '/home/chriskafka/dataset/ShapeNetCompletion/%s/complete/%s/%s.pcd'

    Note that I am using gradient accumulation to simulate a big batchsize(32 in paper), You can set accumulation_steps = 1 in train_*.py under core directoroies to cancel this.

Run

  1. Install and run tensorboard(optional): 
    https://www.tensorflow.org/tensorboard/get_started?hl=zh-cn
  2. Step1: Pretrine PCN. bs 32, 206 epochs, CD Loss descended to 9.917 
    python main_pcn.py --baseline
  3. Step2: Modify __C.CONST.PCNWEIGHTS in config_pcn.py to direct to pre-trained models obtained in step1, 
    __C.CONST.PCNWEIGHTS = 'checkpoint/pcn-baseline-best.pth'

    train ASFM-net.

    python main_pcn.py --backbone

    bs 32, 116 epochs, CD Loss descended to 11.87(12.09 in paper).

Results

  1. Coarse cd loss represention of step2, red for train loss while green for test.

Loss_Epoch_cd_coarse

  1. Fine cd loss represention, green for test in step2 while blue for test in step1.

Loss_Epoch_cd_fine.png

  1. Feature Matching loss represention of step2.

Loss_Epoch_feat_matching.png

  1. Total loss represention of step2.

Loss_Epoch_cd_total.png

Thanks To

PCN pytorch implementation refers to https://github.com/paul007pl/VRCNet.

Point Clouds Utils(FPS, CD) borrowed from https://github.com/AllenXiangX/SnowflakeNet.

Data precessing codes comes from https://github.com/hzxie/GRNet.

And thanks ASFM-Net for their contributions.

If you have any suggestions or ideas about my work, feel free to refer a issue even a pr to keep me informed, thanks again.