Awesome
O-CNN
<!-- ## Introduction <a name="introduction"></a> -->This repository contains the implementation of our papers related with O-CNN.
The code is released under the MIT license.
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O-CNN: Octree-based Convolutional Neural Networks<br/> By Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun and Xin Tong <br/> ACM Transactions on Graphics (SIGGRAPH), 36(4), 2017
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Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes<br/> By Peng-Shuai Wang, Chun-Yu Sun, Yang Liu and Xin Tong<br/> ACM Transactions on Graphics (SIGGRAPH Asia), 37(6), 2018<br/>
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Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion<br/> By Peng-Shuai Wang, Yang Liu and Xin Tong<br/> Computer Vision and Pattern Recognition (CVPR) Workshops, 2020<br/>
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Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination<br/> By Peng-Shuai Wang, Yu-Qi Yang, Qian-Fang Zou, Zhirong Wu, Yang Liu and Xin Tong<br/> AAAI Conference on Artificial Intelligence (AAAI), 2021. [Arxiv, 2020.08]<br/>
If you use our code or models, please cite our paper.
Contents
- Installation
- Data Preparation
- Shape Classification
- Shape Retrieval
- Shape Segmentation
- Shape Autoencoder
- Shape Completion
- Image2Shape
- Unsupverised Pretraining
- ScanNet Segmentation
What's New?
- 2021.08.24: Update the code for pythorch-based O-CNN, including a UNet and some other major components. Our vanilla implementation without any tricks on ScanNet dataset achieves 76.2 mIoU on the ScanNet benchmark, even surpassing the recent state-of-art approaches published in CVPR 2021 and ICCV 2021.
- 2021.03.01: Update the code for pytorch-based O-CNN, including a ResNet and some important modules.
- 2021.02.08: Release the code for ShapeNet segmentation with HRNet.
- 2021.02.03: Release the code for ModelNet40 classification with HRNet.
- 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.
Please contact us (Peng-Shuai Wang wangps@hotmail.com, Yang Liu yangliu@microsoft.com ) if you have any problems about our implementation.