https://github.com/ethanhe42/estimated-depth-map-helps-image-classification

Depth estimation with neural network, and learning on RGBD images
https://github.com/ethanhe42/estimated-depth-map-helps-image-classification

convolutional-neural-networks depth-estimation image-recognition neural-network

Last synced: 5 months ago
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Depth estimation with neural network, and learning on RGBD images

• Host: GitHub
• URL: https://github.com/ethanhe42/estimated-depth-map-helps-image-classification
• Owner: ethanhe42
• License: mit
• Created: 2016-05-23T17:04:55.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2022-02-28T02:27:17.000Z (over 4 years ago)
• Last Synced: 2024-04-14T17:44:44.726Z (about 2 years ago)
• Topics: convolutional-neural-networks, depth-estimation, image-recognition, neural-network
• Language: HTML
• Homepage: https://yihui-he.github.io/blog/estimated-depth-map-helps-image-classification
• Size: 48.2 MB
• Stars: 51
• Watchers: 6
• Forks: 14
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

Awesome Lists containing this project

README

          
# Estimated Depth Map Helps Image Classification

[GitHub - yihui-he/Estimated-Depth-Map-Helps-Image-Classification: Depth estimation with neural network, and learning on RGBD images](https://github.com/yihui-he/Estimated-Depth-Map-Helps-Image-Classification)

[Estimated Depth Map Helps Image Classification](https://arxiv.org/abs/1709.07077)

![Untitled](https://github.com/ethanhe42/Estimated-Depth-Map-Helps-Image-Classification/assets/10027339/a1ec2282-ad31-4013-b78b-361c46fa3837)

![Untitled 1](https://github.com/ethanhe42/Estimated-Depth-Map-Helps-Image-Classification/assets/10027339/13900f3b-0a69-4100-8c78-2b5345b2f3dd)

if you find our work helpful in your research, please consider citing:

```

@article{estimated2017he,

  title={Estimated Depth Map Helps Image Classification},

  author={He, Yihui},

  journal={arXiv preprint arXiv:1709.07077},

  year={2017}

}

```

### how to test

1. you can run tryhere.ipynb to test performance on RGBD and RGB images.

2. you can do depth estimation in train/ folder

### Approach

1. train a mapping map RGB to depth

2. convert cifar10 to images

3. convert RGBD to cifar10 format

4. train neural network on RGBD dataset

### download

[dcnf-fcsp](https://bitbucket.org/fayao/dcnf-fcsp)[RGBD CIFAR-10](https://github.com/yihui-he/Estimated-Depth-Map-Helps-Image-Classification/releases/tag/depth-cifar-10)

### External Links

[all dataset descriptions](http://www0.cs.ucl.ac.uk/staff/M.Firman/RGBDdatasets/)[a good one](http://redwood-data.org/3dscan/index.html)