O-CNN

This repository contains the implementation of O-CNN and Adaptive O-CNN introduced in our SIGGRAPH 2017 paper and SIGGRAPH Asia 2018 paper.
The code is released under the MIT license.

• O-CNN: Octree-based Convolutional Neural Networks
  By Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun and Xin Tong
  ACM Transactions on Graphics (SIGGRAPH), 36(4), 2017

• Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes
  By Peng-Shuai Wang, Chun-Yu Sun, Yang Liu and Xin Tong
  ACM Transactions on Graphics (SIGGRAPH Asia), 37(6), 2018
  

If you use our code or models, please cite our paper.

@article {Wang-2017-OCNN,
    title     = {{O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis}},
    author    = {Wang, Peng-Shuai and Liu, Yang and Guo, Yu-Xiao and Sun, Chun-Yu and Tong, Xin},
    journal   = {ACM Transactions on Graphics (SIGGRAPH)},
    volume    = {36},
    number    = {4},
    year      = {2017},
}
@article {Wang-2018-AOCNN,
    title     = {{Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes}},
    author    = {Wang, Peng-Shuai and Sun, Chun-Yu and Liu, Yang and Tong, Xin},
    journal   = {ACM Transactions on Graphics (SIGGRAPH Asia)},
    volume    = {37},
    number    = {6},
    year      = {2018},
}

Contents

• Installation
• Data Preparation
• Shape Classification
• Shape Retrieval
• Shape Segmentation
• Shape Autoencoder
• Shape Completion
• Image2Shape

We thank the authors of ModelNet, ShapeNet and Region annotation dataset for sharing their 3D model datasets with the public.

Please contact us (Pengshuai Wang [email protected], Yang Liu [email protected] ) if you have any problems about our implementation.