Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model

• Authors: Dat T. Nguyen, André Kaup,

• Affiliation: Friedrich-Alexander University Erlangen-Nuremberg, 91058 Erlangen, Germany

• Accepted to: [IEEE Transactions on Circuits and Systems for Video Technology]

• Links: [Arxiv], [IEEE Xplore])

Description

• Abstract: In this paper, we present an efficient lossless point cloud compression method that uses sparse tensor-based deep neural networks to learn point cloud geometry and color probability distributions. Our method represents a point cloud with both occupancy feature and three attribute features at different bit depths in a unified sparse representation. This allows us to efficiently exploit feature-wise and point-wise dependencies within point clouds using a sparse tensor-based neural network and thus build an accurate auto-regressive context model for an arithmetic coder. To the best of our knowledge, this is the first learning-based lossless point cloud geometry and attribute compression approach. Compared with the-state-of-the-art lossless point cloud compression method from Moving Picture Experts Group (MPEG), our method achieves 22.6% reduction in total bitrate on a diverse set of test point clouds while having 49.0% and 18.3% rate reduction on geometry and color attribute component, respectively.

• This is a Pytorch implementation of attribute compression module from the paper, for the geometry compression module, please refer to SparseVoxelDNN.

Requirments

• pytorch 1.9.0, py3.9_cuda11.1_cudnn8.0.5_0
• MinkowskiEngine 0.5
• torchac 0.9.3
• packages in mink_environment.yml

Getting started

• Install the dependencies using the conda environment.yml file

conda env create --name mink --file environment.yml

Dataset

• We use four datasets with color attributes: MPEG Owlii, MPEG 8i, MVUB, and MPEG CAT1 (except point clouds used for testing). All points clouds are partitioned into a set of points with maximum bounding box of 64.
• Example: the following command performs the partitioning for Owlii dataset:

    python3 -m DataPreprocessing.ds_pc_octree_blocks datasets/Owlii datasets/Owlii_bl64 --vg_size 1024 --level 4 

The partitioned blocks are written into the datasets/Owlii_bl64 directory, doing the same for other datasets

• Randomly split blocks to training and validating set:

    python3 -m DataPreprocessing.ds_select_randomly_move datasets/ datasets/validation/ 0.3

• An example training set is located in ExampleTrainingSet

Training

python3 -m Training.cnet_attribute_training -trainset ../Datasets/CNeT_TrainingSet/33K/  -validset ../Datasets/CNeT_ValidSet/33K/ -flag test -outputmodel Model/  -lr 7 -useDA 8   --color -opt 1 -dim 2 -ngpus 1  -batch 2  -bacc 1

The argument description can be found in the Training/cnet_attribute_training.py

Encoding

python3 -m Encoders.cnet_attribute_encoder -level 10 -ply ply_path -output Output/   -color_voxeldnn_path checkpoint1 -color_voxeldnn_path checkpoint2 -color_voxeldnn_path checkpoint3  -signaling signal

Checkpoints can be downloaded from here