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Depth estimation with neural network, and learning on RGBD images

Home Page: https://yihui-he.github.io/blog/estimated-depth-map-helps-image-classification

License: MIT License

Jupyter Notebook 1.01% Python 1.08% TeX 3.14% MATLAB 6.40% Makefile 0.70% HTML 71.01% C++ 0.41% Cuda 0.80% C 14.47% Shell 0.16% CSS 0.65% Clean 0.07% Roff 0.11%
neural-network depth-estimation image-recognition convolutional-neural-networks

estimated-depth-map-helps-image-classification's Introduction

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

Estimated Depth Map Helps Image Classification

Untitled

Untitled 1

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-fcspRGBD CIFAR-10

External Links

all dataset descriptionsa good one

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