• Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation (SAPCU)
• • Environment
  • Note That
  • Evaluation
  • Trainning
  • Performance
  • • • PyTorch:
      • Tensorflow:
  • Cite
  • contributors

Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation (SAPCU)

This is the code project of Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural
Representation published on CVPR 2022.
The origin project is based on pytorch and we provide the tensorflow version in ./sapcu_tf.

Environment

Python 3.6

PyTorch 1.9.0

Tensorflow 2.3

Tensorlayer3

CUDA 10.2

Note That

you can run the origin pytorch project by the follow commands, while you should

cd ./sapcu_tf

, to train or test the transplanted tensorflow project.

Evaluation

The pretrained models are put in dir ./out.
Firstly, you should compile cpp

g++ -std=c++11 dense.cpp -O2 -o dense

Then, you can run

python generate.py

Trainning

Download the training dataset from the link and unzip it to the working directory.

https://pan.baidu.com/s/1VQ-3RFO02fQfcLBfqvCBZA 
access code: vpfm 

Then run the following commands for training sapcu network

python trainfn.py
python trainfd.py

Performance

The 4X upsampling performance on pytorch and tensorflow platform is statisted in follow table,

PyTorch:

CD	EMD	F-score	AVG	STD
0.010127	0.004974	0.561682	0.003492	0.009351

Tensorflow:

CD	EMD	F-score	AVG	STD
0.012917	0.009506	0.472918	0.008855	0.018178

Cite

Wenbo Zhao, Xianming Liu, Zhiwei Zhong, Junjun Jian, Wei Gao, Ge Li, Xiangyang Ji, “Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation,” Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2022.

contributors

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