This is the code of D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction.
Link of the paper: https://www.ijcai.org/proceedings/2022/0126.pdf
python trainer.py --batch_size=4 --gpu=7 --lamb=10 --exp_name=I10 --dataset_dir='/home/zhaoxudong/dataset_npy'python trainer_lossless.py --dataset_dir='/home/zhaoxudong/dataset_npy'In fact, lossless_coder.pth is a pre-trained model that can be directly used in testing. You probably won't need to retrain it from scratch.
Estimate the bitrate by the differential entropy of the factorized entropy model without practical and separate encoding and decoding processes:
python test_owlii.py --log_name='aaa' --gpu=1 --frame_count=32 --results_dir='results' --tmp_dir='tmp' --dataset_dir='/home/zhaoxudong/Owlii_10bit'Involve actual arithmetic coding that generates real bitstreams, with separate encoding and decoding processes and calculation of encoding and decoding time:
python test_time.py --log_name='aaa' --gpu=1 --frame_count=32 --dataset_dir='/home/zhaoxudong/Owlii_10bit'- If
./GPCC/tmc3: Permission denied:
chmod 777 ./GPCC/tmc3- If
./GPCC/pc_error: Permission denied:
chmod 777 ./GPCC/pc_error- The folder
PCGCv2need to be copied and in both the parent and current directory.
Shown in the folder ddpcc_ckpts_mpeg and ddpcc_ckpts.
ddpcc_ckpts_mpeg is trained on 10-bit 8IVFBv2 dataset and can be tested on Owlii dataset. Detailed information is shown in the MPEG proposal: M60267 “[AI-3DGC] D-DPCC Test Results on 10 bit Owlii”, 2022/7. Results on 10-bit Owlii dataset are shown in the folder results_csv.
ddpcc_ckpts is trained on Owlii and tested on 8IVFBv2. Detailed information is shown in the paper.
If you want to cite our work, please use the following reference:
@inproceedings{ijcai2022p126, title = {D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction}, author = {Fan, Tingyu and Gao, Linyao and Xu, Yiling and Li, Zhu and Wang, Dong}, booktitle = {Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, {IJCAI-22}}, publisher = {International Joint Conferences on Artificial Intelligence Organization}, editor = {Lud De Raedt}, pages = {898--904}, year = {2022}, month = {7}, note = {Main Track}, doi = {10.24963/ijcai.2022/126}, url = {https://doi.org/10.24963/ijcai.2022/126}, }
(Also shown in requirements.txt)
cuda~=11.5.50
numpy~=1.21.2
open3d~=0.14.1
pandas~=1.2.3
torch~=1.10.0
MinkowskiEngine~=0.5.4
pytorch3d~=0.6.1
tqdm~=4.62.3
tensorboardX~=2.5
matplotlib~=3.5.1
h5py~=3.6.0
torchac~=0.9.3
setuptools~=58.0.4
scipy~=1.7.3
scikit-learn~=1.0.2