Reducing the Complexity of Normalizing Flow Architectures for Point Cloud Attribute Compression

Project Information

• Authors: Rodrigo Borba Pinheiro^1,2, Jean-Eudes Marvie¹, Giuseppe Valenzise², Frederic Dufaux²
• Affiliations: ¹InterDigital, Inc,²Université Paris-Saclay, CNRS, CentraleSupélec, L2S 91190 Gif-sur-Yvette, France

Introduction

This repository contains the implementation of RNF-PCAC, an improved NF architecture with reduced complexity. It is composed of two operating modes specialized for low and high bitrates, combined in a rate-distortion optimized fashion. Our approach reduces the number of parameters of the existing NF architectures by over 6×. At the same time, it achieves state-of-the-art coding gains compared to previous learning-based methods and, for some point clouds, it matches the performance of G-PCC (v.21).

Implemented architectures:

• RNF-PCAC: BP Mode

• RNF-PCAC: NA Mode

Requirements

Please refer to the requirements.txt file on the project for the necessary python packages.

• MPEG G-PCC codec mpeg-pcc-tmc13: necessary to compare results with G-PCC and to obtain the metric config files.

• MPEG metric software mpeg-pcc-dmetric, available on the MPEG Gitlab, you need to register and request the permissions for MPEG/PCC: necessary to obtain PSNR values. (Can be replaced by other metric calculation)

How to use

To get the help for the arguments of each file, simply use (replace "file" by the desired file to get help):

python file.py --help

Training

To train new models edit the config file to reflect the architecture you want. The train_config file lets you customize the type of architecture according to their availability, choose the training dataset path and the testing dataset path. Besides you can control the number of filters of intermediate layers for the architecture.

To train all the models in the train_config file, simply run:

python train_all.py --config train_config.yaml

Evaluating on models

Edit the eval_config.yaml file to reflect your paths:

• MODEL_PATH: Folder where the weights of the trained models were saved
• MPEG_TMC13_DIR: G-PCC folder (mpeg-pcc-tmc13)
• PCERROR: mpeg-pcc-dmetric folder
• MPEG_DATASET_DIR: MPEG PCC dataset folder
• EXPERIMENT_DIR: Experiment folder, all results are saved in this folder

python eval_all_.py --config eval_config.yaml

This will run the models in the config file through all the point clouds specified in the "Data" part of the .yaml

Simple Inference for a single model

An example of command to run to perform inference in a single point cloud with the wanted model. Make sure the model configuration reflects the checkpoint path to be loaded.

To encode:

python main.py --command encode --input_file input_pointcloud.ply --output_file input_pointcloud.bin --model_name model_name --arch_type RNF --color_space RGB --squeeze_type avg --N_levels 3 --M 128 --enh_channels 64 --attention_channels 128 --model_path ../checkpoint.pth.tar

python main.py --command decode --input_file input_pointcloud.bin --output_file reconstructed.ply --model_name model_name --arch_type RNF --color_space RGB --squeeze_type avg --N_levels 3 --M 128 --enh_channels 64 --attention_channels 128 --model_path ../checkpoint.pth.tar --geo input_pointcloud.ply

References

[1] R. B. Pinheiro, J. -E. Marvie, G. Valenzise and F. Dufaux, "Reducing the Complexity of Normalizing Flow Architectures for Point Cloud Attribute Compression," ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, Republic of, 2024, pp. 8170-8174, doi: 10.1109/ICASSP48485.2024.10446754.

[2] R. B. Pinheiro, J. -E. Marvie, G. Valenzise and F. Dufaux, "NF-PCAC: Normalizing Flow Based Point Cloud Attribute Compression," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10096294.

A special thanks to @mauriceqch for providing a base for our code in pcc_geo_cnn_v2.

[3] M. Quach, G. Valenzise and F. Dufaux, "Improved Deep Point Cloud Geometry Compression," 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), Tampere, Finland, 2020, pp. 1-6, doi: 10.1109/MMSP48831.2020.9287077.

Cite This Work

Please cite our work if you find it useful for your research:

@INPROCEEDINGS{pinheiro2023rnf,
  author={Pinheiro, Rodrigo B. and Marvie, Jean-Eudes and Valenzise, Giuseppe and Dufaux, Frédéric},
  booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Reducing the Complexity of Normalizing Flow Architectures for Point Cloud Attribute Compression}, 
  year={2024},
  volume={},
  number={},
  pages={8170-8174},
  keywords={Learning systems;Point cloud compression;Bit rate;Rate-distortion;Signal processing;Encoding;Complexity theory;Point clouds;Learning-Based;Compression;Attributes;Normalizing Flow},
  doi={10.1109/ICASSP48485.2024.10446754}}