OpenPointCloud/NGS_Test

• NGS_dengy02
• • Introduction to NGS
  • Test indicators
  • Contributions
  • performance
  • Citation
  • Contributor

NGS_dengy02

Introduction to NGS

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.

source code is here: https://github.com/linyaog/point_based_pcgc

Point cloud geometry (PCG), local neighborhood graph, dynamic filter, attention-based sampling, point-wise convolution

Test indicators

1. Using the shapenetcorev2 dataset used in the paper, four indicators of its 55 categories are tested: Test time (each point cloud), bpp, D1-PSNR, D2-PSNR.
2. Except for the two point cloud categories car and rifle in the paper, the indicators of other categories are selected as the best test values.

Contributions

1. According to different hyperparameters, 12 optimal models were trained (referred to the readpoints folder in detail).

2. According to the optimal model, the Test Times (Each Point Cloud), bpp, D1-PSNR, D2-PSNR of all 55 categories of Shapenetcorev2 DataSet are tested.

performance

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.

Citation

@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}}

Contributor

name: Deng Yu
email: dengy02@pcl.ac.cn