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NYUDepthNet

★ 43 Open GitHub ↗

A Tensorflow interface for the paper: Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture (***Deprecated***)

// repository documentation

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# Deprecated
Please note that this repository is longer functional and only exists for archival purposes. Since the release of this repository, several other approaches (e.g. https://arxiv.org/abs/1609.03677) have produced superior results; therefore, I recommend that you explore these methods instead. Regardless, the model.py provides a "barebones" implementation without weights or display tools.

# DepthNet
DepthNet is an unofficial Tensorflow implementation of *[Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture](https://www.cs.nyu.edu/~deigen/depth/)*. __Note__: This repository was created for a research project, not associated with NYU, to explore the implications of residual neural networks for monocular depth estimation and smartphone-based spatial mapping. These modifications were not included in this repository for compeleteness. If you would like these modification, please email me at rcbridendev@gmail.com.

# Installation
There are two ways to install NYUDepthNet - Automatic and Manual. The latter is complex to configure, so it's recommended that you use the Automatic method.

## Automatic 
This is the recommended way to install NYUDepthNet.
* Clone the repository.
* Install dependencies
	* [Tensorflow](https://github.com/tensorflow/tensorflow)
	* [Numpy](https://github.com/numpy/numpy)
	* [Matplotlib](https://github.com/matplotlib/matplotlib)
* Run main.py to ensure NYUDepthNet was installed correctly.

## Manual
This installation method is more complex; however, it does grant increased customizability.
* Clone the repository.
* Install dependencies
	* [Theano](https://github.com/Theano/Theano)
	* Install the dependencies mentioned in the Automatic method.
* There are two methods to setup the installation:
	1. Run setup_env.py
	2. Manually download an unpack weights
		* Download weights and scripts from [NYU](https://cs.nyu.edu/~deigen/depth/)
		* Convert weights from .pk format to .npy or to tensorflow variables. __NOTE__: These weights are formatted as Theano tensors, so they must be converted to Tensorflow tensors. See [TheanoUnpickler](https://github.com/Rostifar/TheanoUnpickler).
* Run main.py to ensure NYUDepthNet was installed correctly.

# Example Images

![alt text](https://raw.githubusercontent.com/Rostifar/NYUDepthNet/master/img_0004_abs_smooth_new.png)
![alt text](https://raw.githubusercontent.com/Rostifar/NYUDepthNet/master/img_0331_abs_smooth_new.png)
![alt text](https://raw.githubusercontent.com/Rostifar/NYUDepthNet/master/img_0807_abs_smooth_new.png)
![alt text](https://raw.githubusercontent.com/Rostifar/NYUDepthNet/master/img_0815_abs_smooth_new.png)

NYUDepthNet

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