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All Projects → microsoft → O Cnn

microsoft / O Cnn

Licence: mit
O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis

Labels

convolutional-neural-networkscaffe3d-models

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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.

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

What's New?

  • 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.

Contents

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.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].