This repository constains the codes and ShapeNetV1-Surface-Skeleton,ShapNetV1-SkeletalVolume and 2d image datasets ShapeNetRendering. Please download the above datasets at the first, and then put them under the SkeletonNet/data folder.

• If you want to use our skeletal point cloud extraction code, you can download the skeleton extraction code. This code is built on Visual Studio2013 + Qt. Thank VCC @ ShenZhen University for their open source code.
• If you want to convert the skeletal point clouds to skeletal volumes, you can run the below scripts.

python data/prepare_skeletalvolume.py --cats 03001627 --vx_res 32
python data/prepare_skeletalvolume2.py --cats 03001627 --vx_res 64
python data/prepare_skeletalvolume2.py --cats 03001627 --vx_res 128
python data/prepare_skeletalvolume2.py --cats 03001627 --vx_res 256

Before running above scripts, you need to change raw_pointcloud_dir and upsample_skeleton_dir used when extracting skeletal points.

First you need to create an anaconda environment called SkeletonNet using

conda env create -f environment.yaml
conda activate SkeletonNet

For each stage, please follow the README.md under the SkeletonBridge-recon/Explicit_mesh/Implicit_mesh folder.

If you find this work useful in your research, please consider citing:

@InProceedings{Tang_2019_CVPR,
author = {Tang, Jiapeng and Han, Xiaoguang and Pan, Junyi and Jia, Kui and Tong, Xin},
title = {A Skeleton-Bridged Deep Learning Approach for Generating Meshes of Complex Topologies From Single RGB Images},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

If you have any questions, please feel free to contact with Tang Jiapeng msjptang@mail.scut.edu.cn.