Deep Primitive Segmentation Using Hybrid Representations

HPNet

This repository contains the PyTorch implementation of paper: HPNet: Deep Primitive Segmentation Using Hybrid Representations.

Installation

The main experiments are implemented on pytorch 1.7.0, tensorflow 1.15.0. Please install the dependancy packages using pip install -r requirements.txt.

Dataset

ABCParts Dataset

ABCParts Dataset is made by ParseNet. We reorganize the dataset to fit our dataloader and clean some wrong labels. Please download the dataset here(69G) and put it under data/ABC folder.

Usage

To train our model on ABC dataset: run

python train.py --data_path=./path/to/dataset`

To evaluate our model on ABC dataset: run

python train.py --eval --checkpoint_path=./path/to/pretrained/model --val_skip=100

on the subset of test dataset. To test on the full dataset, simply set val_skip=1.

pretrained models

We provide pre-trained model on ABC Dataset here. This should generate the result reported in the paper.

Acknowledgements

We would like to thank and acknowledge referenced codes from

ParseNet: https://github.com/Hippogriff/parsenet-codebase.
DGCNN: https://github.com/WangYueFt/dgcnn.

Citations

If you find this repository useful in your research, please cite:

@article{yan2021hpnet,
  title={HPNet: Deep Primitive Segmentation Using Hybrid Representations},
  author={Yan, Siming and Yang, Zhenpei and Ma, Chongyang and Huang, Haibin and Vouga, Etienne and Huang, Qixing},
  journal={arXiv preprint arXiv:2105.10620},
  year={2021}
}