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README.md

pcc_geo_cnn_v1_yh

key words: lossy, point cloud, geometry compression, basic entropy coding model

pcc_geo_cnn_v1, is a kind of point cloud lossy compression method. It is the earliest 3D entropy coding method based on deeplearning. The network is simple, and shallow, with analysis transform, entropy bottleneck, and synthesis transform models. The performance is just a little bit better than GPCC octree.

our contributions

1.transplant from tensorflow to pytorch
2.benchmark test on both tensorflow and pytorch, and compare the performance
3.pre-trained model for Tensorflow version.

file structure

root
└── pytorch: pytorch code, models included
└── TensorFlow source code
└── LEARNING CONVOLUTIONAL TRANSFORMS FOR LOSSY POINT CLOUD GEOMETRY.pdf: origional paper
└── datasets: see the ModelNet40_pc_64.zip in dataset part
└── TFmodel_testfile: pre-trained model and test files for tensorflow version

environment

  1. pytorch
  • ubuntu 18.04
  • cuda V10.2.89
  • python 3.6.9
  • refer to requirements-pytorch.txt

command

  • pytorch:

cd pytorch

training:

python train.py

encode:

python compress.py --input_file “/userhome/PCGCv1/pytorch_eval/28_airplane_0270.ply”

You can get some test files here.

decode:

python decompress.py --input_file “output/28_airplane_0270.bin”

performance

  • benchmark test on tensorflow and pytorch below. From the result, we can see that for dense PCs, the method can achieve good compression rate, while for sparse PCs, the performance is not so good.
  • Compared the performance of tensorflow and pytorch, the bpp of pytorch is smaller while the D1 and D2 are also smaller than tensorflow. So their performances are closed to each other.

Table 1. Test on TensorFlow

file bpp mseF,PSNR (p2point) mseF,PSNR (p2plane)
sarah_vox9_0023_n.ply 1.538 64.552 67.941
Phil_vox9_0139_n.ply 1.418 64.168 67.501
ricardo_vox9_0215_n.ply 1.775 64.852 68.22
david_vox9_0215_n.ply 1.496 64.857 68.25
andrew_vox9_0317_n.ply 1.43 64.262 67.521
28_airplane_0270_n.ply 5.137 58.029 61.357
3_lamp_0073_n.ply 4.464 60.014 64.062
average 2.465 62.962 66.407

Table 2. Test on PyTorch

file bpp mseF,PSNR (p2point) mseF,PSNR (p2plane)
sarah_vox9_0023_n.ply 1.375 61.865 64.474
Phil_vox9_0139_n.ply 1.192 61.495 64.005
ricardo_vox9_0215_n.ply 1.851 62.234 64.797
david_vox9_0215_n.ply 1.402 62.233 64.895
andrew_vox9_0317_n.ply 1.416 61.718 64.174
28_airplane_0270_n.ply 4.433 54.361 56.293
3_lamp_0073_n.ply 4.099 55.646 58.536
average 2.253 59.936 62.453

Cite from:

@inproceedings{DBLP:conf/icip/QuachVD19,
author = {Maurice Quach and
Giuseppe Valenzise and
Fr{'{e}}d{'{e}}ric Dufaux},
title = {Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression},
booktitle = {2019 {IEEE} International Conference on Image Processing, {ICIP} 2019,
Taipei, Taiwan, September 22-25, 2019},
pages = {4320--4324},
publisher = {{IEEE}},
year = {2019},
url = {https://doi.org/10.1109/ICIP.2019.8803413},
doi = {10.1109/ICIP.2019.8803413},
timestamp = {Wed, 11 Dec 2019 16:30:23 +0100},
biburl = {https://dblp.org/rec/conf/icip/QuachVD19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

contributors

name: Ye Hua
email: yeh@pcl.ac.cn

简介

lossy point cloud geometry compression, basic entropy coding model

Unity3D Asset Python Markdown

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