https://github.com/Microsoft/O-CNN

O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis
https://github.com/Microsoft/O-CNN

convolutional-neural-networks o-cnn octree

Last synced: 3 months ago
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O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis

• Host: GitHub
• URL: https://github.com/Microsoft/O-CNN
• Owner: microsoft
• License: mit
• Created: 2017-06-13T04:03:54.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-08-30T09:45:52.000Z (about 2 years ago)
• Last Synced: 2024-08-01T03:29:08.110Z (3 months ago)
• Topics: convolutional-neural-networks, o-cnn, octree
• Language: C++
• Homepage:
• Size: 9.27 MB
• Stars: 720
• Watchers: 37
• Forks: 170
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md
  • Security: SECURITY.md

Awesome Lists containing this project

README

        
# O-CNN

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](https://wang-ps.github.io/O-CNN.html)**


  By [Peng-Shuai Wang](https://wang-ps.github.io/), [Yang Liu](https://xueyuhanlang.github.io/), 

  Yu-Xiao Guo, Chun-Yu Sun and [Xin Tong](https://www.microsoft.com/en-us/research/people/xtong/) 


  ACM Transactions on Graphics (SIGGRAPH), 36(4), 2017

- **[Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes](https://wang-ps.github.io/AO-CNN.html)**


By [Peng-Shuai Wang](https://wang-ps.github.io/), Chun-Yu Sun, [Yang Liu](https://xueyuhanlang.github.io/) 

and [Xin Tong](https://www.microsoft.com/en-us/research/people/xtong/)


ACM Transactions on Graphics (SIGGRAPH Asia), 37(6), 2018


- **[Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion](https://arxiv.org/abs/2006.03762)**


By [Peng-Shuai Wang](https://wang-ps.github.io/), [Yang Liu](https://xueyuhanlang.github.io/) 

and [Xin Tong](https://www.microsoft.com/en-us/research/people/xtong/)


Computer Vision and Pattern Recognition (CVPR) Workshops, 2020


- **[Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination](https://arxiv.org/abs/2008.01068)**


By [Peng-Shuai Wang](https://wang-ps.github.io/), Yu-Qi Yang, Qian-Fang Zou, 

[Zhirong Wu](https://www.microsoft.com/en-us/research/people/wuzhiron/), 

[Yang Liu](https://xueyuhanlang.github.io/) 

and [Xin Tong](https://www.microsoft.com/en-us/research/people/xtong/)


AAAI Conference on Artificial Intelligence (AAAI), 2021. [Arxiv, 2020.08]


If you use our code or models, please [cite](docs/citation.md) our paper.

### Contents

- [Installation](docs/installation.md)

- [Data Preparation](docs/data_preparation.md)

- [Shape Classification](docs/classification.md)

- [Shape Retrieval](docs/retrieval.md)

- [Shape Segmentation](docs/segmentation.md)

- [Shape Autoencoder](docs/autoencoder.md)

- [Shape Completion](docs/completion.md)

- [Image2Shape](docs/image2shape.md)

- [Unsupverised Pretraining](docs/unsupervised.md)

- [ScanNet Segmentation](docs/scannet.md)

### 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](docs/scannet.md) dataset achieves 76.2 mIoU on the 

  [ScanNet benchmark](http://kaldir.vc.in.tum.de/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](docs/classification.md#o-cnn-on-pytorch).

- 2020.08.16: We released our code for [3D unsupervised learning](docs/unsupervised.md).

  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](docs/segmentation.md#shape-segmentation-on-partnet-with-tensorflow).

  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](docs/completion.md).

  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 [email protected], Yang Liu [email protected] ) 

if you have any problems about our implementation.