GitHub - ialhashim/DenseDepth: High Quality Monocular Depth Estimation via Transfer Learning

High Quality Monocular Depth Estimation via Transfer Learning (arXiv 2018)

[Update] Our latest method with better performance can be found here AdaBins.

Offical Keras (TensorFlow) implementaiton. If you have any questions or need more help with the code, contact the first author.

[Update] Added a Colab notebook to try the method on the fly.

[Update] Experimental TensorFlow 2.0 implementation added.

[Update] Experimental PyTorch code added.

Results

KITTI

NYU Depth V2

Requirements

This code is tested with Keras 2.2.4, Tensorflow 1.13, CUDA 10.0, on a machine with an NVIDIA Titan V and 16GB+ RAM running on Windows 10 or Ubuntu 16.
Other packages needed keras pillow matplotlib scikit-learn scikit-image opencv-python pydot and GraphViz for the model graph visualization and PyGLM PySide2 pyopengl for the GUI demo.
Minimum hardware tested on for inference NVIDIA GeForce 940MX (laptop) / NVIDIA GeForce GTX 950 (desktop).
Training takes about 24 hours on a single NVIDIA TITAN RTX with batch size 8.

Pre-trained Models

NYU Depth V2 (165 MB)
KITTI (165 MB)

Demos

After downloading the pre-trained model (nyu.h5), run python test.py. You should see a montage of images with their estimated depth maps.
[Update] A Qt demo showing 3D point clouds from the webcam or an image. Simply run python demo.py. It requires the packages PyGLM PySide2 pyopengl.

Data

NYU Depth V2 (50K) (4.1 GB): You don't need to extract the dataset since the code loads the entire zip file into memory when training.
KITTI: copy the raw data to a folder with the path '../kitti'. Our method expects dense input depth maps, therefore, you need to run a depth inpainting method on the Lidar data. For our experiments, we used our Python re-implmentaiton of the Matlab code provided with NYU Depth V2 toolbox. The entire 80K images took 2 hours on an 80 nodes cluster for inpainting. For our training, we used the subset defined here.
Unreal-1k: coming soon.

Training

Run python train.py --data nyu --gpus 4 --bs 8.

Evaluation

Download, but don't extract, the ground truth test data from here (1.4 GB). Then simply run python evaluate.py.

Reference

Corresponding paper to cite:

@article{Alhashim2018,
author = {Ibraheem Alhashim and Peter Wonka},
title = {High Quality Monocular Depth Estimation via Transfer Learning},
journal = {arXiv e-prints},
volume = {abs/1812.11941},
year = {2018},
url = {https://arxiv.org/abs/1812.11941},
eid = {arXiv:1812.11941},
eprint = {1812.11941}
}

High Quality Monocular Depth Estimation via Transfer Learning (arXiv 2018)

Ibraheem Alhashim and Peter Wonka

[Update] Our latest method with better performance can be found here AdaBins.

Offical Keras (TensorFlow) implementaiton. If you have any questions or need more help with the code, contact the first author.

[Update] Added a Colab notebook to try the method on the fly.

[Update] Experimental TensorFlow 2.0 implementation added.

[Update] Experimental PyTorch code added.

Results

KITTI

NYU Depth V2

Requirements

This code is tested with Keras 2.2.4, Tensorflow 1.13, CUDA 10.0, on a machine with an NVIDIA Titan V and 16GB+ RAM running on Windows 10 or Ubuntu 16.
Other packages needed keras pillow matplotlib scikit-learn scikit-image opencv-python pydot and GraphViz for the model graph visualization and PyGLM PySide2 pyopengl for the GUI demo.
Minimum hardware tested on for inference NVIDIA GeForce 940MX (laptop) / NVIDIA GeForce GTX 950 (desktop).
Training takes about 24 hours on a single NVIDIA TITAN RTX with batch size 8.

Pre-trained Models

NYU Depth V2 (165 MB)
KITTI (165 MB)

Demos

After downloading the pre-trained model (nyu.h5), run python test.py. You should see a montage of images with their estimated depth maps.
[Update] A Qt demo showing 3D point clouds from the webcam or an image. Simply run python demo.py. It requires the packages PyGLM PySide2 pyopengl.

Data

NYU Depth V2 (50K) (4.1 GB): You don't need to extract the dataset since the code loads the entire zip file into memory when training.
KITTI: copy the raw data to a folder with the path '../kitti'. Our method expects dense input depth maps, therefore, you need to run a depth inpainting method on the Lidar data. For our experiments, we used our Python re-implmentaiton of the Matlab code provided with NYU Depth V2 toolbox. The entire 80K images took 2 hours on an 80 nodes cluster for inpainting. For our training, we used the subset defined here.
Unreal-1k: coming soon.

Training

Run python train.py --data nyu --gpus 4 --bs 8.

Evaluation

Download, but don't extract, the ground truth test data from here (1.4 GB). Then simply run python evaluate.py.

Reference

Corresponding paper to cite:

@article{Alhashim2018,
author = {Ibraheem Alhashim and Peter Wonka},
title = {High Quality Monocular Depth Estimation via Transfer Learning},
journal = {arXiv e-prints},
volume = {abs/1812.11941},
year = {2018},
url = {https://arxiv.org/abs/1812.11941},
eid = {arXiv:1812.11941},
eprint = {1812.11941}
}

High Quality Monocular Depth Estimation via Transfer Learning (arXiv 2018)

Ibraheem Alhashim and Peter Wonka

[Update] Our latest method with better performance can be found here AdaBins.

Offical Keras (TensorFlow) implementaiton. If you have any questions or need more help with the code, contact the first author.

[Update] Added a Colab notebook to try the method on the fly.

[Update] Experimental TensorFlow 2.0 implementation added.

[Update] Experimental PyTorch code added.

Results

KITTI

NYU Depth V2

Requirements

This code is tested with Keras 2.2.4, Tensorflow 1.13, CUDA 10.0, on a machine with an NVIDIA Titan V and 16GB+ RAM running on Windows 10 or Ubuntu 16.
Other packages needed keras pillow matplotlib scikit-learn scikit-image opencv-python pydot and GraphViz for the model graph visualization and PyGLM PySide2 pyopengl for the GUI demo.
Minimum hardware tested on for inference NVIDIA GeForce 940MX (laptop) / NVIDIA GeForce GTX 950 (desktop).
Training takes about 24 hours on a single NVIDIA TITAN RTX with batch size 8.

Pre-trained Models

NYU Depth V2 (165 MB)
KITTI (165 MB)

Demos

After downloading the pre-trained model (nyu.h5), run python test.py. You should see a montage of images with their estimated depth maps.
[Update] A Qt demo showing 3D point clouds from the webcam or an image. Simply run python demo.py. It requires the packages PyGLM PySide2 pyopengl.

Data

NYU Depth V2 (50K) (4.1 GB): You don't need to extract the dataset since the code loads the entire zip file into memory when training.
KITTI: copy the raw data to a folder with the path '../kitti'. Our method expects dense input depth maps, therefore, you need to run a depth inpainting method on the Lidar data. For our experiments, we used our Python re-implmentaiton of the Matlab code provided with NYU Depth V2 toolbox. The entire 80K images took 2 hours on an 80 nodes cluster for inpainting. For our training, we used the subset defined here.
Unreal-1k: coming soon.

Training

Run python train.py --data nyu --gpus 4 --bs 8.

Evaluation

Download, but don't extract, the ground truth test data from here (1.4 GB). Then simply run python evaluate.py.

Reference

Corresponding paper to cite:

@article{Alhashim2018,
author = {Ibraheem Alhashim and Peter Wonka},
title = {High Quality Monocular Depth Estimation via Transfer Learning},
journal = {arXiv e-prints},
volume = {abs/1812.11941},
year = {2018},
url = {https://arxiv.org/abs/1812.11941},
eid = {arXiv:1812.11941},
eprint = {1812.11941}
}

High Quality Monocular Depth Estimation via Transfer Learning (arXiv 2018)

Ibraheem Alhashim and Peter Wonka

[Update] Our latest method with better performance can be found here AdaBins.

Offical Keras (TensorFlow) implementaiton. If you have any questions or need more help with the code, contact the first author.

[Update] Added a Colab notebook to try the method on the fly.

[Update] Experimental TensorFlow 2.0 implementation added.

[Update] Experimental PyTorch code added.

Results

KITTI

NYU Depth V2

Requirements

This code is tested with Keras 2.2.4, Tensorflow 1.13, CUDA 10.0, on a machine with an NVIDIA Titan V and 16GB+ RAM running on Windows 10 or Ubuntu 16.
Other packages needed keras pillow matplotlib scikit-learn scikit-image opencv-python pydot and GraphViz for the model graph visualization and PyGLM PySide2 pyopengl for the GUI demo.
Minimum hardware tested on for inference NVIDIA GeForce 940MX (laptop) / NVIDIA GeForce GTX 950 (desktop).
Training takes about 24 hours on a single NVIDIA TITAN RTX with batch size 8.

Pre-trained Models

NYU Depth V2 (165 MB)
KITTI (165 MB)

Demos

After downloading the pre-trained model (nyu.h5), run python test.py. You should see a montage of images with their estimated depth maps.
[Update] A Qt demo showing 3D point clouds from the webcam or an image. Simply run python demo.py. It requires the packages PyGLM PySide2 pyopengl.

Data

NYU Depth V2 (50K) (4.1 GB): You don't need to extract the dataset since the code loads the entire zip file into memory when training.
KITTI: copy the raw data to a folder with the path '../kitti'. Our method expects dense input depth maps, therefore, you need to run a depth inpainting method on the Lidar data. For our experiments, we used our Python re-implmentaiton of the Matlab code provided with NYU Depth V2 toolbox. The entire 80K images took 2 hours on an 80 nodes cluster for inpainting. For our training, we used the subset defined here.
Unreal-1k: coming soon.

Training

Run python train.py --data nyu --gpus 4 --bs 8.

Evaluation

Download, but don't extract, the ground truth test data from here (1.4 GB). Then simply run python evaluate.py.

Reference

Corresponding paper to cite:

@article{Alhashim2018,
author = {Ibraheem Alhashim and Peter Wonka},
title = {High Quality Monocular Depth Estimation via Transfer Learning},
journal = {arXiv e-prints},
volume = {abs/1812.11941},
year = {2018},
url = {https://arxiv.org/abs/1812.11941},
eid = {arXiv:1812.11941},
eprint = {1812.11941}
}

High Quality Monocular Depth Estimation via Transfer Learning (arXiv 2018)

Ibraheem Alhashim and Peter Wonka

[Update] Our latest method with better performance can be found here AdaBins.

Offical Keras (TensorFlow) implementaiton. If you have any questions or need more help with the code, contact the first author.

[Update] Added a Colab notebook to try the method on the fly.

[Update] Experimental TensorFlow 2.0 implementation added.

[Update] Experimental PyTorch code added.

Results

KITTI

NYU Depth V2

Requirements

This code is tested with Keras 2.2.4, Tensorflow 1.13, CUDA 10.0, on a machine with an NVIDIA Titan V and 16GB+ RAM running on Windows 10 or Ubuntu 16.
Other packages needed keras pillow matplotlib scikit-learn scikit-image opencv-python pydot and GraphViz for the model graph visualization and PyGLM PySide2 pyopengl for the GUI demo.
Minimum hardware tested on for inference NVIDIA GeForce 940MX (laptop) / NVIDIA GeForce GTX 950 (desktop).
Training takes about 24 hours on a single NVIDIA TITAN RTX with batch size 8.

Pre-trained Models

NYU Depth V2 (165 MB)
KITTI (165 MB)

Demos

After downloading the pre-trained model (nyu.h5), run python test.py. You should see a montage of images with their estimated depth maps.
[Update] A Qt demo showing 3D point clouds from the webcam or an image. Simply run python demo.py. It requires the packages PyGLM PySide2 pyopengl.

Data

NYU Depth V2 (50K) (4.1 GB): You don't need to extract the dataset since the code loads the entire zip file into memory when training.
KITTI: copy the raw data to a folder with the path '../kitti'. Our method expects dense input depth maps, therefore, you need to run a depth inpainting method on the Lidar data. For our experiments, we used our Python re-implmentaiton of the Matlab code provided with NYU Depth V2 toolbox. The entire 80K images took 2 hours on an 80 nodes cluster for inpainting. For our training, we used the subset defined here.
Unreal-1k: coming soon.

Training

Run python train.py --data nyu --gpus 4 --bs 8.

Evaluation

Download, but don't extract, the ground truth test data from here (1.4 GB). Then simply run python evaluate.py.

Reference

Corresponding paper to cite:

@article{Alhashim2018,
author = {Ibraheem Alhashim and Peter Wonka},
title = {High Quality Monocular Depth Estimation via Transfer Learning},
journal = {arXiv e-prints},
volume = {abs/1812.11941},
year = {2018},
url = {https://arxiv.org/abs/1812.11941},
eid = {arXiv:1812.11941},
eprint = {1812.11941}
}

High Quality Monocular Depth Estimation via Transfer Learning (arXiv 2018)

Ibraheem Alhashim and Peter Wonka

[Update] Our latest method with better performance can be found here AdaBins.

Offical Keras (TensorFlow) implementaiton. If you have any questions or need more help with the code, contact the first author.

[Update] Added a Colab notebook to try the method on the fly.

[Update] Experimental TensorFlow 2.0 implementation added.

[Update] Experimental PyTorch code added.

Results

KITTI

NYU Depth V2

Requirements

This code is tested with Keras 2.2.4, Tensorflow 1.13, CUDA 10.0, on a machine with an NVIDIA Titan V and 16GB+ RAM running on Windows 10 or Ubuntu 16.
Other packages needed keras pillow matplotlib scikit-learn scikit-image opencv-python pydot and GraphViz for the model graph visualization and PyGLM PySide2 pyopengl for the GUI demo.
Minimum hardware tested on for inference NVIDIA GeForce 940MX (laptop) / NVIDIA GeForce GTX 950 (desktop).
Training takes about 24 hours on a single NVIDIA TITAN RTX with batch size 8.

Pre-trained Models

NYU Depth V2 (165 MB)
KITTI (165 MB)

Demos

After downloading the pre-trained model (nyu.h5), run python test.py. You should see a montage of images with their estimated depth maps.
[Update] A Qt demo showing 3D point clouds from the webcam or an image. Simply run python demo.py. It requires the packages PyGLM PySide2 pyopengl.

Data

NYU Depth V2 (50K) (4.1 GB): You don't need to extract the dataset since the code loads the entire zip file into memory when training.
KITTI: copy the raw data to a folder with the path '../kitti'. Our method expects dense input depth maps, therefore, you need to run a depth inpainting method on the Lidar data. For our experiments, we used our Python re-implmentaiton of the Matlab code provided with NYU Depth V2 toolbox. The entire 80K images took 2 hours on an 80 nodes cluster for inpainting. For our training, we used the subset defined here.
Unreal-1k: coming soon.

Training

Run python train.py --data nyu --gpus 4 --bs 8.

Evaluation

Download, but don't extract, the ground truth test data from here (1.4 GB). Then simply run python evaluate.py.

Reference

Corresponding paper to cite:

@article{Alhashim2018,
author = {Ibraheem Alhashim and Peter Wonka},
title = {High Quality Monocular Depth Estimation via Transfer Learning},
journal = {arXiv e-prints},
volume = {abs/1812.11941},
year = {2018},
url = {https://arxiv.org/abs/1812.11941},
eid = {arXiv:1812.11941},
eprint = {1812.11941}
}

High Quality Monocular Depth Estimation via Transfer Learning (arXiv 2018)

Ibraheem Alhashim and Peter Wonka

[Update] Our latest method with better performance can be found here AdaBins.

Offical Keras (TensorFlow) implementaiton. If you have any questions or need more help with the code, contact the first author.

[Update] Added a Colab notebook to try the method on the fly.

[Update] Experimental TensorFlow 2.0 implementation added.

[Update] Experimental PyTorch code added.

Results

KITTI

NYU Depth V2

Requirements

This code is tested with Keras 2.2.4, Tensorflow 1.13, CUDA 10.0, on a machine with an NVIDIA Titan V and 16GB+ RAM running on Windows 10 or Ubuntu 16.
Other packages needed keras pillow matplotlib scikit-learn scikit-image opencv-python pydot and GraphViz for the model graph visualization and PyGLM PySide2 pyopengl for the GUI demo.
Minimum hardware tested on for inference NVIDIA GeForce 940MX (laptop) / NVIDIA GeForce GTX 950 (desktop).
Training takes about 24 hours on a single NVIDIA TITAN RTX with batch size 8.

Pre-trained Models

NYU Depth V2 (165 MB)
KITTI (165 MB)

Demos

After downloading the pre-trained model (nyu.h5), run python test.py. You should see a montage of images with their estimated depth maps.
[Update] A Qt demo showing 3D point clouds from the webcam or an image. Simply run python demo.py. It requires the packages PyGLM PySide2 pyopengl.

Data

NYU Depth V2 (50K) (4.1 GB): You don't need to extract the dataset since the code loads the entire zip file into memory when training.
KITTI: copy the raw data to a folder with the path '../kitti'. Our method expects dense input depth maps, therefore, you need to run a depth inpainting method on the Lidar data. For our experiments, we used our Python re-implmentaiton of the Matlab code provided with NYU Depth V2 toolbox. The entire 80K images took 2 hours on an 80 nodes cluster for inpainting. For our training, we used the subset defined here.
Unreal-1k: coming soon.

Training

Run python train.py --data nyu --gpus 4 --bs 8.

Evaluation

Download, but don't extract, the ground truth test data from here (1.4 GB). Then simply run python evaluate.py.

Reference

Corresponding paper to cite:

@article{Alhashim2018,
author = {Ibraheem Alhashim and Peter Wonka},
title = {High Quality Monocular Depth Estimation via Transfer Learning},
journal = {arXiv e-prints},
volume = {abs/1812.11941},
year = {2018},
url = {https://arxiv.org/abs/1812.11941},
eid = {arXiv:1812.11941},
eprint = {1812.11941}
}

High Quality Monocular Depth Estimation via Transfer Learning (arXiv 2018)

Ibraheem Alhashim and Peter Wonka

[Update] Our latest method with better performance can be found here AdaBins.

Offical Keras (TensorFlow) implementaiton. If you have any questions or need more help with the code, contact the first author.

[Update] Added a Colab notebook to try the method on the fly.

[Update] Experimental TensorFlow 2.0 implementation added.

[Update] Experimental PyTorch code added.

Results

KITTI

NYU Depth V2

Requirements

This code is tested with Keras 2.2.4, Tensorflow 1.13, CUDA 10.0, on a machine with an NVIDIA Titan V and 16GB+ RAM running on Windows 10 or Ubuntu 16.
Other packages needed keras pillow matplotlib scikit-learn scikit-image opencv-python pydot and GraphViz for the model graph visualization and PyGLM PySide2 pyopengl for the GUI demo.
Minimum hardware tested on for inference NVIDIA GeForce 940MX (laptop) / NVIDIA GeForce GTX 950 (desktop).
Training takes about 24 hours on a single NVIDIA TITAN RTX with batch size 8.

Pre-trained Models

NYU Depth V2 (165 MB)
KITTI (165 MB)

Demos

After downloading the pre-trained model (nyu.h5), run python test.py. You should see a montage of images with their estimated depth maps.
[Update] A Qt demo showing 3D point clouds from the webcam or an image. Simply run python demo.py. It requires the packages PyGLM PySide2 pyopengl.

Data

NYU Depth V2 (50K) (4.1 GB): You don't need to extract the dataset since the code loads the entire zip file into memory when training.
KITTI: copy the raw data to a folder with the path '../kitti'. Our method expects dense input depth maps, therefore, you need to run a depth inpainting method on the Lidar data. For our experiments, we used our Python re-implmentaiton of the Matlab code provided with NYU Depth V2 toolbox. The entire 80K images took 2 hours on an 80 nodes cluster for inpainting. For our training, we used the subset defined here.
Unreal-1k: coming soon.

Training

Run python train.py --data nyu --gpus 4 --bs 8.

Evaluation

Download, but don't extract, the ground truth test data from here (1.4 GB). Then simply run python evaluate.py.

Reference

Corresponding paper to cite:

@article{Alhashim2018,
author = {Ibraheem Alhashim and Peter Wonka},
title = {High Quality Monocular Depth Estimation via Transfer Learning},
journal = {arXiv e-prints},
volume = {abs/1812.11941},
year = {2018},
url = {https://arxiv.org/abs/1812.11941},
eid = {arXiv:1812.11941},
eprint = {1812.11941}
}

Name		Name	Last commit message	Last commit date
PyTorch		PyTorch
Tensorflow		Tensorflow
examples		examples
.gitignore		.gitignore
DenseDepth.ipynb		DenseDepth.ipynb
LICENSE		LICENSE
README.md		README.md
augment.py		augment.py
callbacks.py		callbacks.py
data.py		data.py
demo.py		demo.py
demo_depth.npy		demo_depth.npy
demo_rgb.npy		demo_rgb.npy
evaluate.py		evaluate.py
fill_depth_colorization.py		fill_depth_colorization.py
layers.py		layers.py
loss.py		loss.py
model.py		model.py
test.py		test.py
train.py		train.py
utils.py		utils.py

Name		Name	Last commit message	Last commit date
PyTorch		PyTorch
Tensorflow		Tensorflow
examples		examples
.gitignore		.gitignore
DenseDepth.ipynb		DenseDepth.ipynb
LICENSE		LICENSE
README.md		README.md
augment.py		augment.py
callbacks.py		callbacks.py
data.py		data.py
demo.py		demo.py
demo_depth.npy		demo_depth.npy
demo_rgb.npy		demo_rgb.npy
evaluate.py		evaluate.py
fill_depth_colorization.py		fill_depth_colorization.py
layers.py		layers.py
loss.py		loss.py
model.py		model.py
test.py		test.py
train.py		train.py
utils.py		utils.py

Name		Name	Last commit message	Last commit date
PyTorch		PyTorch
Tensorflow		Tensorflow
examples		examples
.gitignore		.gitignore
DenseDepth.ipynb		DenseDepth.ipynb
LICENSE		LICENSE
README.md		README.md
augment.py		augment.py
callbacks.py		callbacks.py
data.py		data.py
demo.py		demo.py
demo_depth.npy		demo_depth.npy
demo_rgb.npy		demo_rgb.npy
evaluate.py		evaluate.py
fill_depth_colorization.py		fill_depth_colorization.py
layers.py		layers.py
loss.py		loss.py
model.py		model.py
test.py		test.py
train.py		train.py
utils.py		utils.py

Name		Name	Last commit message	Last commit date
PyTorch		PyTorch
Tensorflow		Tensorflow
examples		examples
.gitignore		.gitignore
DenseDepth.ipynb		DenseDepth.ipynb
LICENSE		LICENSE
README.md		README.md
augment.py		augment.py
callbacks.py		callbacks.py
data.py		data.py
demo.py		demo.py
demo_depth.npy		demo_depth.npy
demo_rgb.npy		demo_rgb.npy
evaluate.py		evaluate.py
fill_depth_colorization.py		fill_depth_colorization.py
layers.py		layers.py
loss.py		loss.py
model.py		model.py
test.py		test.py
train.py		train.py
utils.py		utils.py

Name		Name	Last commit message	Last commit date
PyTorch		PyTorch
Tensorflow		Tensorflow
examples		examples
.gitignore		.gitignore
DenseDepth.ipynb		DenseDepth.ipynb
LICENSE		LICENSE
README.md		README.md
augment.py		augment.py
callbacks.py		callbacks.py
data.py		data.py
demo.py		demo.py
demo_depth.npy		demo_depth.npy
demo_rgb.npy		demo_rgb.npy
evaluate.py		evaluate.py
fill_depth_colorization.py		fill_depth_colorization.py
layers.py		layers.py
loss.py		loss.py
model.py		model.py
test.py		test.py
train.py		train.py
utils.py		utils.py

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High Quality Monocular Depth Estimation via Transfer Learning (arXiv 2018)

Results

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Pre-trained Models

Demos

Data

Training

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High Quality Monocular Depth Estimation via Transfer Learning (arXiv 2018)

Results

Requirements

Pre-trained Models

Demos

Data

Training

Evaluation

Reference

About

Topics

Resources

Stars

Watchers

Forks

Releases

Packages

Used by

Contributors

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Latest commit

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High Quality Monocular Depth Estimation via Transfer Learning (arXiv 2018)

Results

Requirements

Pre-trained Models

Demos

Data

Training

Evaluation

Reference

About

Topics

Resources

Stars

Watchers

Forks

Releases

Packages

Used by

Contributors

Languages

Latest commit

History

Folders and files

Repository files navigation

High Quality Monocular Depth Estimation via Transfer Learning (arXiv 2018)

Results

Requirements

Pre-trained Models