• PU-Net: Point Cloud Upsampling Network.
• • Algorithm analysis
  • Running project
  • Performance comparison
  • Contributors

PU-Net: Point Cloud Upsampling Network.

PU-Net is a point cloud upsampling algorithm based on deep learning, which is collected into our open source algorithm library of point cloud. The original project is run on python2 and tensorflow-1.3, which is in folder './src'. In order to make algorithm support multiple frameworks, we attempt to expande the PyTorch version which is in folder './punet_pytorch'. Besides we upgrade original codes with python3, tensorflow-1.14 and CUDA-10, which is in folder './src3'.

Algorithm analysis

please refer to "PU-Net algorithm analysis.doc"

Running project

please refer to the file 'requirements.txt' and README.md under each version.

Performance comparison

We compare the algorithm performance of different frameworks under tensorflow and pytorch respectively. The main evaluation metric is NUC (refer to the paper for details). Note: we take "D=40" in our calculation.

Paper:

p	0.002	0.004	0.006	0.008	0.010	0.012
NUC	0.174	0.138	0.122	0.115	0.112	0.110

TF:

p	0.002	0.004	0.006	0.008	0.010	0.012	0.015
NUC	0.20004781	0.16366877	0.14697995	0.13717446	0.13099714	0.12588924	0.12350797

PyTorch:

p	0.002	0.004	0.006	0.008	0.010	0.012	0.015
NUC	0.17464873	0.14155353	0.12641019	0.11760626	0.11187568	0.11156724	0.11221573

Contributors

name: Zhang Yongchi
email: zhangych02@pcl.ac.cn