O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis
This repository contains the implementation of our papers related with O-CNN.
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
Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion
By Peng-Shuai Wang, Yang Liu
and Xin Tong
Computer Vision and Pattern Recognition (CVPR) Workshops, 2020
Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination
By Peng-Shuai Wang, Yu-Qi Yang, Qian-Fang Zou,
Zhirong Wu,
Yang Liu
and Xin Tong
AAAI Conference on Artificial Intelligence (AAAI), 2021. [Arxiv, 2020.08]
If you use our code or models, please cite our paper.
Please contact us (Peng-Shuai Wang [email protected], Yang Liu [email protected] ) if you have any problems about our implementation.