• PCGCv1
• • our contributions
  • files
  • environment
  • • 1.pytorch
    • 2.tensorlayer
  • command
  • • 1.pytorch
    • 2.tensorlayer
  • performance
  • Cite from:
  • contributors

PCGCv1

PCGCv1, point cloud compression, deeplearning, hyper
PCGCv1 is a kind of lossy point cloud compression method. It uses hyper priors to improve performance. The source code uses Tensorflow as deeplearning framework, here we transplant to tensorlayer and pytorch.

Paper Citation:
@ARTICLE{9321375, author={Wang, Jianqiang and Zhu, Hao and Liu, Haojie and Ma, Zhan}, journal={IEEE Transactions on Circuits and Systems for Video Technology}, title={Lossy Point Cloud Geometry Compression via End-to-End Learning}, year={2021}, volume={31}, number={12}, pages={4909-4923}, doi={10.1109/TCSVT.2021.3051377}}

our contributions

1.transplant from TensorFlow to tensorlayer and pytorch.
2.benchmark tests on many PCs, including those not tested by author.
3.BD-BR and BD-PSNR calculation over GPCC(octree), also compared to trisoup.
4.draw a flow chart about encoding process to help better understanding.
5.compare performance between different frameworks.

files

1.trainingset：refer to points64_part1.zip
2.testset：refer to here
3.PCGCv1-master：source code and models on tensorflow
4.pytorch：code and model file on pytorch
5.author results：test results on TF by author
6.tensorlayer：code and model file on tensorlayer
7.Lossy Point Cloud Geometry Compression via End-to-End Learning.pdf：origional paper
8.PCGCv1 performance.xlsx：performance test result between different frameworks
9.PCGCv1 explanation.docx：introduction for paper, migration, instructions
10.flowchart.vsdx：flow chart about encoding process
11.results: test results by contributors

environment

1.pytorch

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

2.tensorlayer

• Ubuntu 18.04
• cuda 10.1.243
• python 3.7.6
• tensorflow-gpu 2.3.1
• tensorlayer3 1.2.0
• torch 1.8.1
• torchac 0.9.3
• refer to pytorch/requirements-pytorch.txt

command

1.pytorch

cd pytorch
chmod +x myutils/tmc3
chmod +x myutils/pc_error_d

training:

python train.py

encode:

python test.py --command=compress --input=".../longdress_vox10_1300.ply" --ckpt_dir="ckpts/epoch_13_13599_a6b3.pth" --batch_parallel=64

decode:

python test.py --command=decompress --input="compressed/longdress_vox10_1300" --ckpt_dir="ckpts/epoch_13_13599_a6b3.pth"

evaluate:

python eval.py --input ".../longdress_vox10_1300.ply" --ckpt_dir="ckpts/epoch_13_13599_a6b3.pth"

2.tensorlayer

cd tensorlayer
chmod +x myutils/tmc3
chmod +x myutils/pc_error_d

training:

python train.py

encode:

python test.py --command=compress --input=".../longdress_vox10_1300.ply" --ckpt_dir="ckpts/epoch_30_30349_a6b3.npz" --batch_parallel=4

decode:

python test.py --command=decompress --input="compressed/longdress_vox10_1300" --ckpt_dir="ckpts/epoch_30_30349_a6b3.npz"

evaluate:

python eval.py --input ".../longdress_vox10_1300.ply" --ckpt_dir="ckpts/epoch_30_30349_a6b3.npz"

performance

We first caculate the BD-PSNR and BD-BR rate of PCGCv1 and GPCC trisoup over GPCC octree on many PCs, the result shows as below. We can see from the table, that for dense PC with bit width of 10 and 11, PCGCv1 gets better performance over octree and trisoup, while for sparse or vox12 PC, it gets worse. The main reason is that there is no PC data in training sets with similar distributions or geometry features.

We then carry out a benchmark test on PC files including those are not tested by author. The part of the result shows below. For other results, please go to results/

PC	ori_points	mseF,PSNR (p2plane)	mseF,PSNR (p2point)	time_enc	time_dec	bpp
sarah_vox9_0023	299363	57.7055	50.695	33.071	17.38	0.0889
sarah_vox9_0023	299363	64.4054	61.5184	26.308	12.194	0.1417
sarah_vox9_0023	299363	67.2605	63.8802	27.383	12.014	0.2254
sarah_vox9_0023	299363	68.5138	65.0884	36.251	16.083	0.2746
sarah_vox9_0023	299363	69.2561	65.7159	34.676	12.428	0.3361
sarah_vox9_0023	299363	69.9792	66.3444	32.836	12.619	0.4053
sarah_vox9_0023	299363	70.3887	66.4396	32.061	15.015	0.5022

According to the results above, we draw D1-bpp and D2-bpp figures, to clearly show the performance between different PC files as below. It is obvious that, PCs of vox11 have best performance, which are on up-left part of the figure, PCs of vox10 are second in the middle, and PCs of sparse have bad performance on the bottom part.

We also make tests to compare the performances between different frameworks of TensorFlow, PyTorch, TensorLayer on testsets. From the results below, we can see that TF has the biggest bpp and best D1 and D2. Overall, the performance of TF is better than the other two. PCGCv1 is hard to train, as it has hyper-prior structure, especially for pytorch.

PC file	PT_D2	PT_D1	PT_time_enc	PT_time_dec	PT_bpp	TL_D2	TL_D1	TL_time_enc	TL_time_dec	TL_bpp	TF_D2	TF_D1	TF_time_enc	TF_time_dec	TF_bpp
longdress_vox10_1300	78.141	74.402	23.247	14.524	0.461	78.355	74.623	30.629	11.171	0.409	79.056	75.103	111.515	64.833	0.475
loot_vox10_1200	78.37	74.725	23.033	15.752	0.461	78.548	74.881	28.628	11.067	0.407	79.307	75.438	93.721	47.03	0.478
phil_vox9_0139	69.506	66.086	9.933	6.691	0.437	69.949	66.342	13.689	4.218	0.404	69.841	66.004	30.855	15.075	0.49
phil_vox10_0139	74.888	71.174	42.176	25.102	0.406	75.3	71.423	51.019	17.319	0.382	75.134	70.848	109.787	54.19	0.462
queen_vox10_0200	78.858	75.301	25.637	14.562	0.397	79.287	75.612	33.443	10.5	0.363	79.744	76.062	84.323	42.428	0.409
redandblack_vox10_1550	77.414	73.69	21.179	13.991	0.467	77.59	73.925	26.341	9.637	0.415	78.133	74.161	73.224	39.963	0.491
sarah_vox9_0023	70.132	66.582	9.43	6.487	0.448	70.477	66.796	13.958	5.954	0.412	70.389	66.44	32.061	15.015	0.502
sarah_vox10_0023	75.243	71.426	36.756	23.853	0.408	75.6	71.599	44.684	17.633	0.382	75.429	71.121	107.195	47.374	0.461
soldier_vox10_0690	78.25	74.52	28.984	18.417	0.463	78.429	74.702	36.533	13.448	0.411	79.17	75.329	109.487	53.836	0.486

Cite from:

https://github.com/NJUVISION/PCGCv1
The source code files are provided by Nanjing University Vision Lab. And thanks for the help from SJTU Cooperative Medianet Innovation Center. Please contact us (wangjq@smail.nju.edu.cn) if you have any questions.

contributors

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