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yehua 36d61a6a68 | 1 year ago | |
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pytorch | 1 year ago | |
LICENSE | 1 year ago | |
Neural Network Modeling of Probabilities for Coding the Octree Representation of Point Clouds.pdf | 2 years ago | |
OpenPointCloud-logo.png | 1 year ago | |
README-old.md | 1 year ago | |
README.md | 1 year ago | |
requirements-pytorch.txt | 1 year ago |
key words:point cloud, lossless geometry compression, deeplearning, multi resolution compression
NNCTX, is a kind of point cloud lossless compression method. Based on downsampling, the method build a multi-resolution point cloud sets, and compresses/decompresses from the lowest resolution to original point cloud. The bpp is better than GPCC octree, but worse than OctAttention.
Paper Citation: arXiv:2106.06482 [cs.CV]
https://doi.org/10.48550/arXiv.2106.06482
1.transplant from tensorflow to pytorch
2.benchmark test on both tensorflow and pytorch, and compare the performance
root
└── pytorch: pytorch code, models included
└── tensorflow source code: please go to: https://github.com/marmus12/NNCTX
└── Neural Network Modeling of Probabilities for Coding the Octree Representation of Point Clouds.pdf: origional paper
└── datasets: see the datasets.zip in dataset part
cd pytorch
training:
python train_torch.py
encode && decode:
python main_enc_dec.py --input "/userhome/PCGCv1/pytorch_eval/28_airplane_0270.ply" --ckpt_dir 'train_logs/20220401-155532/epoch_72.pth'
You can get some test files from here
Table 1. Test on TensorFlow
file | dense or sparse | ori_level | enc_time | dec_time | bpv |
---|---|---|---|---|---|
sarah_vox10_0023.ply | dense | 10 | 106.212 | 111.231 | 0.965 |
sarah_vox9_0023.ply | dense | 9 | 24.928 | 25.871 | 0.929 |
phil_vox10_0139.ply | dense | 10 | 128.019 | 136.108 | 1.021 |
phil_vox9_0139.ply | dense | 9 | 29.077 | 30.929 | 0.997 |
redandblack_vox10_1550.ply | dense | 10 | 71.721 | 74.898 | 0.885 |
queen_vox10_0200.ply | dense | 10 | 80.169 | 85.792 | 0.919 |
soldier_vox10_0690.ply | dense | 10 | 101.22 | 108.956 | 0.8 |
longdress_vox10_1300.ply | dense | 10 | 77.13 | 83.583 | 0.778 |
basketball_player_vox11_00000200.ply | dense | 11 | 264.752 | 283.019 | 0.691 |
loot_vox10_1200.ply | dense | 10 | 73.748 | 79.241 | 0.761 |
dancer_vox11_00000001.ply | dense | 11 | 234.587 | 249.559 | 0.691 |
vox10_002719.ply | sparse | 10 | 8.688 | 9.453 | 4.868 |
vox10_000001.ply | sparse | 10 | 11.549 | 12.512 | 5.441 |
vox10_002215.ply | sparse | 10 | 8.782 | 9.555 | 6.27 |
vox10_001406.ply | sparse | 10 | 9.298 | 10.348 | 5.374 |
vox10_000832.ply | sparse | 10 | 11.278 | 12.009 | 6.243 |
vox10_000355.ply | sparse | 10 | 9.058 | 9.23 | 5.48 |
Table 2. Test on PyTorch
file | dense or sparse | ori_level | enc_time | dec_time | bpv |
---|---|---|---|---|---|
sarah_vox10_0023.ply | dense | 10 | 168.12 | 169.809 | 0.982 |
sarah_vox9_0023.ply | dense | 9 | 49.754 | 51.095 | 0.944 |
phil_vox10_0139.ply | dense | 10 | 194.698 | 212.601 | 1.034 |
phil_vox9_0139.ply | dense | 9 | 62.802 | 64.654 | 1.005 |
redandblack_vox10_1550.ply | dense | 10 | 194.372 | 214.579 | 0.895 |
queen_vox10_0200.ply | dense | 10 | 139.459 | 140.515 | 0.936 |
soldier_vox10_0690.ply | dense | 10 | 249.87 | 229.195 | 0.813 |
longdress_vox10_1300.ply | dense | 10 | 202.623 | 233.049 | 0.788 |
basketball_player_vox11_00000200.ply | dense | 11 | 480.024 | 497.199 | 0.697 |
loot_vox10_1200.ply | dense | 10 | 201.843 | 199.929 | 0.772 |
dancer_vox11_00000001.ply | dense | 11 | 474.928 | 447.583 | 0.697 |
vox10_002719.ply | sparse | 10 | 29.227 | 29.637 | 4.926 |
vox10_000001.ply | sparse | 10 | 42.099 | 44.3 | 5.498 |
vox10_002215.ply | sparse | 10 | 25.084 | 25.329 | 6.358 |
vox10_001406.ply | sparse | 10 | 27.075 | 27.182 | 5.454 |
vox10_000832.ply | sparse | 10 | 32.687 | 33.469 | 6.343 |
vox10_000355.ply | sparse | 10 | 25.294 | 24.922 | 5.561 |
name: Ye Hua
email: yeh@pcl.ac.cn
point cloud lossless geometry compression, deeplearning, multi resolution compression
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