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.
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
Arxiv preprint, 2020
If you use our code or models, please cite our paper.
- 2020.10.12: Release the initial version of our O-CNN under PyTorch. The code has been tested with the classification task.
- 2020.08.16: We released our code for 3D unsupervised learning. We provided a unified network architecture for generic shape analysis tasks and an unsupervised method to pretrain the network. Our method achieved state-of-the-art performance on several benchmarks.
- 2020.08.12: We released our code for Partnet segmentation. We achieved an average IoU of 58.4, significantly better than PointNet (IoU: 35.6), PointNet++ (IoU: 42.5), SpiderCNN (IoU: 37.0), and PointCNN(IoU: 46.5).
- 2020.08.05: We released our code for shape completion. We proposed a simple yet efficient network and output-guided skip connections for 3D completion, which achieved state-of-the-art performances on several benchmarks.
- 2020.03.16: We released ResNet-based O-CNN architecture for shape classification. We achieved a testing accuracy of 92.5 on ModelNet40 (without voting).
- Data Preparation
- Shape Classification
- Shape Retrieval
- Shape Segmentation
- Shape Autoencoder
- Shape Completion