GDN-Pytorch

This repository is a Pytorch implementation of the paper "Depth Estimation From a Single Image Using Guided Deep Network"

Minsoo Song and Wonjun Kim
IEEE Access

When using this code in your research, please cite the following paper:

Minsoo Song and Wonjun Kim, "Depth estimation from a single image using guided deep network," IEEE Access, vol. 7, pp. 142595-142606, Dec. 2019.

@ARTICLE{8854079,
author={M. {Song} and W. {Kim}},
journal={IEEE Access},
title={Depth Estimation From a Single Image Using Guided Deep Network},
year={2019},
volume={7},
pages={142595-142606},
doi={10.1109/ACCESS.2019.2944937},}

Model architecture

Requirements

• Python >= 3.5
• Pytorch 0.4.0
• Ubuntu 16.04
• CUDA 8 (if CUDA available)
• cuDNN (if CUDA available)

Video

                                      RGB input                                                                     Estimated depth map

Results

1st  row: RGB input
2nd row: Ground truth
3rd row: Eigen et al.
4th row: Godard et al.
5th row: Kuznietsov et al.
6th row: Ours

Pretrained models

You can download pretrained color-to-depth model

• Trained with KITTI

Demo (Single Image Prediction)

Demo Command Line:

############### Example of argument usage #####################
python depth_extract.py --gpu_num 0,1 --model_dir your/model/path/model.pkl 
## '--gpu_num' argument is index of your available gpu list.        ex) os.environ["CUDA_VISIBLE_DEVICES"]= args.gpu_num

Try it on your own image!

1. Insert your example images(png, jpg) in GDN-pytorch/example/demo_input
   (Since our model was trained at 128 x 416 scale, we recommend resizing the images to the corresponding scale before running the demo.)
2. Specify the model directory, then run the demo.

Dataset

• KITTI
  Official kitti dataset is available on the link.

We prepared the training data by referring to the method on this link.

Training

Training method

Depth_to_depth network training -> Color_to_depth network training(using pretrained depth_to_depth network)

• Depth_to_depth network training

python GDN_main.py ./your/dataset/path --epochs 50 --batch_size 20 --gpu_num 0,1,2,3 --mode DtoD

• Color_to_depth network training

python GDN_main.py ./your/dataset/path --epochs 50 --batch_size 20 --model_dir /your/pretrained/depth_to_depth/model/path --gpu_num 0,1,2,3 --mode RtoD

--gpu_num is index of your available gpu list.

Testing (Eigen split)

• Depth_to_depth network testing

python GDN_main.py /mnt/MS/AEdepth/data_backup --epochs 0 --batch_size 8 --evaluate --real_test --gpu_num 0,1,2,3 --model_dir /your/pretrained/depth_to_depth/model/path --mode DtoD_test --img_save

• Color_to_depth network testing

python GDN_main.py /mnt/MS/AEdepth/data_backup --epochs 0 --batch_size 8 --evaluate --real_test --gpu_num 0,1,2,3 --RtoD_model_dir /your/pretrained/color_to_depth/model/path --mode RtoD_test --img_save

--gpu_num is index of your available gpu list.
if you want save your test result, using --img_save