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TFmodel_testfile | 1 year ago | |
pytorch | 1 year ago | |
LEARNING CONVOLUTIONAL TRANSFORMS FOR LOSSY POINT CLOUD GEOMETRY.pdf | 2 years ago | |
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result.xlsx | 2 years ago |
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.
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.
root
└── pytorch: pytorch code, models included
└── TensorFlow source code
└── LEARNING CONVOLUTIONAL TRANSFORMS FOR LOSSY POINT CLOUD GEOMETRY.pdf: origional paper
└── datasets: follow the instructions to convert them into training sets (ModelNet40_pc_64).
└── TFmodel_testfile: pre-trained model and test files for tensorflow version
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"
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 |
@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}
}
name: Ye Hua
email: yeh@pcl.ac.cn
lossy point cloud geometry compression, basic entropy coding model
Unity3D Asset Python Markdown
MIT
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