There are two branches named obj
and lidar
that implement Object and LiDAR point cloud coding respectively. They share the same network. Paper.
- python 3.7
- PyTorch 1.9.0+cu102
file/environment.sh
to help you build this environment
-
SemanticKITTI (80G)
23201/20351 frames in 00-10/11-21 folders for training/testing.MPEG 8iVFBv2 (5.5GB)
300/300 frames in soldier10 and longdress10 for training
300/300 frames in loot10 and redandblack10 for testingMPEG 8iVFB (100M)
1/1/1/1 frame in Boxer9/10 and Thaidancer9/10 (quantized from 12bit data) for testingJPEG MVUB (8GB)
318/216/207 frames in andrew10, david10 and sarah10 for training
245/245/216/216 frames in Phil9/10 and Ricardo9/10 for testing
(Note: We rotated the MVUB data to make it consistent with MPEG 8i. Please setrotation=True
in thedataPrepare
function when processing MVUB data in training and testing.)
Please set oriDir
in dataPrepare.py
before.
python dataPrepare.py
To prepare train and test data. It will generate *.mat
data in the directory Data
.
python octAttention.py
You should set the Network parameters expName,DataRoot
etc. in networkTool.py
.
This will output checkpoint in expName
folder, e.g. Exp/Kitti
. (Note: You should run DataFolder.calcdataLenPerFile()
in dataset.py
for a new dataset, and you can comment it after you get the parameter dataLenPerFile
)
You may need to run the following command to provide pc_error
and tmc13v14
execute permission.
chmod +x file/pc_error file/tmc13v14
python encoder.py
This will output binary codes saved in .bin
format in Exp(expName)/data
, and will generate *.mat
data in the directory Data/testPly
.
python decoder.py
This will load *.mat
data for check and calculate PSNR by pc_error
.
We provide the test code for TMC13 v14 (G-PCC) for Object and LiDAR point cloud compression.
python testTMC.py
If this work is useful for your research, please consider citing :
@inproceedings{OctAttention,
title={OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression},
author={Fu, Chunyang and Li, Ge and Song, Rui and Gao, Wei and Liu, Shan},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
year={2022}
}