Are you sure you want to delete this task? Once this task is deleted, it cannot be recovered.
dengy02 1cd61d4867 | 1 year ago | |
---|---|---|
checkpoints | 1 year ago | |
models | 1 year ago | |
test | 1 year ago | |
utils | 1 year ago | |
README.md | 1 year ago | |
bitEstimator.py | 1 year ago | |
dataset.py | 1 year ago | |
test.py | 1 year ago | |
train.py | 1 year ago | |
utils.py | 1 year ago |
The NGS comprises of three consecutive steps: first, constructing a local graph of each point using its K nearest neighbors based on the Euclidean distance metric; second, for each local graph, aggregating neighbor weights using point-wise dynamic filter to the graph center point as its feature attribute, by which embedding local structural/geometric variations as the latent features; finally, devising attention-based sampling to all points that having neighboring structures aggregated, to select a subset of points for compact and precise representation of input point cloud.
Point cloud geometry (PCG), local neighborhood graph, dynamic filter, attention-based sampling, point-wise convolution
@INPROCEEDINGS{9506631, author={Gao, Linyao and Fan, Tingyu and Wan, Jianqiang and Xu, Yiling and Sun, Jun and Ma, Zhan}, booktitle={2021 IEEE International Conference on Image Processing (ICIP)}, title={Point Cloud Geometry Compression Via Neural Graph Sampling}, year={2021}, volume={}, number={}, pages={3373-3377}, doi={10.1109/ICIP42928.2021.9506631}}
According to different hyperparameters, 12 optimal models were trained (referred to the readpoints folder in detail).
According to the optimal model, the Test Times (Each Point Cloud), BP, D1-PSNR, D2-PSNR of all 55 categories of Shapenetcorev2 DataSet are tested.
The test results are shown in the following table:
Note that my hardware configuration is different from Paper:
The GPUs of the paper: Nvidia GeForce GTX 1080 Ti.
The CPU of the paper: an Intel Xeon CPU E5-2683 v4.
The GPUs of my test: TESLA T4.
The CPU of my test : Intel(R) Xeon(R) Gold 6248 CPU.
name: Deng Yu
email: dengy02@pcl.ac.cn
论文POINT CLOUD GEOMETRY COMPRESSION VIA NEURAL GRAPH SAMPLING的pytorch版本代码,测试性能
Python
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》