jokieleung / TransPCC

the Pytorch implementation for our ICMR 2022 paper "TransPCC: Towards Deep Point Cloud Compression via Transformers"

TransPCC: Towards Deep Point Cloud Compression via Transformers

This repository implements the algorithms described in our paper TransPCC: Towards Deep Point Cloud Compression via Transformers.

How to get started (using Docker)

Dependenices nvida-docker

Install nvida-docker and follow these instructions

Data

You can download the dataset from here povided by depoco and link the dataset to the docker container by configuring the Makefile

DATASETS=<path-to-your-data>

Building the docker container

For building the Docker Container simply run

make build

in the root directory.

Running the Code

The first step is to run the docker container:

make run

The following commands assume to be run inside the docker container.

Training

For training a network we first have to create the config file with all the parameters. An example of this can be found in /depoco/config/transPCC_demo.yaml. Make sure to give each config file a unique experiment_id: ... to not override previous models. To train the network simply run

python3 transPCC_trainer.py -cfg <path-to-your-config>

Evaluation

Evaluating the network on the test set can be done by:

python3 evaluate.py -cfg <path-to-your-config>

All results will be saved in a dictonary.

Citation

If you find this paper helps your research, please kindly consider citing our paper in your publications.

@inproceedings{liang2022transpcc,
  title={TransPCC: Towards Deep Point Cloud Compression via Transformers},
  author={Liang, Zujie and Liang, Fan},
  booktitle={Proceedings of the 2022 International Conference on Multimedia Retrieval (ICMR)},
  year={2022}
}

Acknowledgment

This repo contains code modified from depoco, Many thanks for their efforts.