PCC Arena

⛔️ DEPRECATED

This repository will not be maintained in the future.

*** Please refer to https://github.com/nmsl-nthu/PCCArena for the latest version. ***

Environments

Ubuntu 20.04

Prerequisites

• git
• gcc
• g++
• cmake
• subversion
• xvfb
• libblas-dev
• libatlas-base-dev
• nvidia-cuda-toolkit

sudo apt install git gcc g++ cmake subversion xvfb libblas-dev libatlas-base-dev nvidia-cuda-toolkit -y

• Ananconda 3

wget https://repo.anaconda.com/archive/Anaconda3-2020.11-Linux-x86_64.sh
sh Anaconda3-2020.11-Linux-x86_64.sh
source .bashrc

Quick Start

To download and set up PCC Arena, please type the following commands.

• Step 1: Clone the github project.

git clone https://github.com/xtorker/PCCArena.git

• Step 2: Change the current directory.

cd PCCArena

• Step 3: Create the conda environment pcc_arena.

conda env create -f cfgs/conda_env/pcc_arena.yml

• Step 4: Activate the environment pcc_arena.

conda activate pcc_arena

• Step 5: Set up the environments.

python setup.py

• Step 6: Grant executed permission.

chmod +x setup_env_ds.sh

• Step 7: Run environment setup script.

./setup_env_ds.sh

• Step 8: Download the pretrained models of GeoCNNv1 and GeoCNNv2 using the following links. Save them in the current directory. GeoCNNv1: https://drive.google.com/file/d/1ayWqNHwvihAludemIF2l9NZSi6ofWHYy/view?usp=sharing GeoCNNv2: https://drive.google.com/file/d/1w5jue_dgR8Xw3D5gvZV1lXDA36NO5T_4/view?usp=sharing

• Step 9: Uncompress the models into algorithms/GeoCNNv1 and algorithms/GeoCNNv2.

tar xvf geocnn_v1_pretrained_models.tar -C algorithms/GeoCNNv1
tar -Jxvf geocnn_v2_pretrained_models.tar.xz -C algorithms/GeoCNNv2

• Step 10: Run experiments in PCC Arena. We have two types of python files for experimenting. One is a short version for testing, and the other is a full version. The short version only runs one compression rate for each algorithm and doesn't run the algorithms which require lots of memory (e.g., GeoCNNv1 requires more than 50GB).

# Short version
python run_experiments_short.py

# Full version
python run_experiments.py

• Step 11: Check the results (binaries, point cloud, metrics) in expereiments/{algorithm}/{rate}

Setup Demo Video

https://youtu.be/tIOUSJMDAUU

Add More PCC Algorithms

1. Put the whole PCC algorithm project folder under algorithms/
2. Write a specific wrapper for it and put it under algs_wrapper/
3. Write a YAML file for configuring any coding parameters and rate control parameters, and put it under cfgs/algs/
4. (Optional) If the PCC algorithm needs specific virtual environment, make sure to indicate the python path in the YAML file (Step 3).