This is the reference Tensorflow implementation for training and testing depth estimation models using the method described in

ICAR 2019 "Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy Maps"

Nícolas dos Santos Rosa, Vitor Guizilini, Valdir Grassi Jr

Citation

If you find our work useful in your research please consider citing our paper:

@INPROCEEDINGS{8981652, 
author={N. d. S. {Rosa} and V. {Guizilini} and V. {Grassi}}, 
booktitle={2019 19th International Conference on Advanced Robotics (ICAR)}, 
title={Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy Maps}, 
year={2019}, 
volume={}, 
number={}, 
pages={793-800}, 
keywords={}, 
doi={10.1109/ICAR46387.2019.8981652}, 
ISSN={null}, 
month={Dec},}

DISCLAIMER

This repository was originally forked from iro-cp/FCRN-DepthPrediction. We developed all the code in Tensorflow for the training step, alongside several modifications for allowing the code to handle different datasets like ApolloScape, KITTI, NYUDepth, and other features that were not available on the original repository.

We used and preserved the network proposed by Laina et al. (2016) presented in the "Deeper Depth Prediction with Fully Convolutional Residual Networks (FCRN)" article. All rights reserved to them.

This code is for non-commercial use. For more information, please see the license clauses.

TODO:

• Describe the commands to compile the CVPP Framework
• Describe the commands to generate the continuous depth maps

Real-Time Depth Map Inference	Real-Time Pointcloud Inference

--debug, enables the Debug Mode. Default= False

--machine, identifies the current machine: nicolas or olorin. Default= nicolas

-m , selects the running mode of the developed framework: train, test or pred. Default= train --model_name, selects the network topology. Default= fcrn

--gpu, specifies the GPU id to run the code. Default= 0

Arguments and flags descriptions:

--retrain, enables the Retrain Mode. Default= False

-s/--dataset, argument selects the desired dataset for training: apolloscape, kitti_depth, kitti_discrete, kitti_continuous, nyudepth, or lrmjose.

--px, argument selects which pixels to optimize: all or valid. Default= valid

--loss, argument selects the desired loss function: mse, berhu, eigen, eigen_grads, etc. Default= berhu

--batch_size, argument specifies the training batch size. Default= 4

--max_steps, argument specifies the max number of training steps. Default= 300000

-l/--learning_rate, defines the initial value of the learning rate. Default= 1e-4

-d/--dropout, enables dropout in the model during training. Default= 0.5 --ldecay, enables learning decay. Default= False -n/--l2norm', enables L2 Normalization. Default= False --data_aug, enables Data Augmentation. Default= True

--remove_sky, removes sky for KITTI Datasets. Default= False

--full_summary, If set, it will keep more data for each summary. Warning: the file can become very large.

--log_directory, sets the directory to save checkpoints and summaries. Default= log_tb/

-t/--show_train_progress, shows the training images progress. Default= False

-v/--show_valid_progress, shows the validation images progress. Default= False

Command line:

$ python3 predict_nick.py --machine nicolas -m train --gpu 0 -s kitti_continuous --px valid --loss berhu --max_steps 300000 -l 1e-4 -d 0.5 --ldecay --l2norm --data_aug --remove_sky -t -v

$ cd tensorflow
$ tensorboard --logdir=tensorflow/output/fcrn/

Arguments and flags descriptions:

--eval_tool, selects the evaluation tool for computing metrics: monodepth or kitti_depth. Default= ''

--test_split, selects the desired test split for evaluation: kitti_stereo, eigen, or eigen_kitti_depth. Default= ''

The --test_split flag allows you to choose which dataset you want to test on.

• kitti_stereo corresponds to the 200 official training set pairs from KITTI stereo 2015.
• eigen corresponds to the 697 test images used by Eigen NIPS14 and uses the raw LIDAR points.
• eigen_kitti_depth corresponds to the 652 test images used by Aleotti arXiv 2018 and uses ground truth semi-dense annotated depth images.

--test_file_path, evaluates the model for the speficied images from a test_file--debug', action='store_true', help="Enables the Debug Mode", default=False) .txt file. Default=''

--min_depth, specifies the minimum depth for evaluation. Default= 1e-3 --max_depth, specifies the maximum depth for evaluation'. Default= 80 --eigen_crop, If set, crops according to Eigen NIPS14. --garg_crop, If set, crops according to Garg ECCV16. Warning: The results on the Eigen split are usually cropped, which you can do by passing the --garg_crop flag.

-o/--output_directory, sets the output directory for test disparities, if empty outputs to checkpoint folder. Default= ''

-u/--show_test_results, shows the network predictions for the specified test split images. Default= False

Command line, when selecting a desired trained model:

$ python3 predict_nick.py --machine nicolas -m test --gpu 0 -s kitti_continuous -r output/fcrn/2018-02-26_17-08-45/restore/model.fcrn --eval_tool monodepth --test_split eigen_kitti_depth -u

Using official evaluation tool from KITTI Depth Prediction Dataset:

$ python3 predict_nick.py -m test --gpu 0 -s kitti_continuous --eval_tool kitti_depth --test_split eigen_kitti_depth -u

Using Monodepth's evaluation code:

$ python3 predict_nick.py -m test --gpu 0 -s kitti_continuous --eval_tool monodepth --test_split eigen_kitti_depth -u

Arguments and flags descriptions:

-r/ --model_path, sets the path to a specific model to be restored. Default= '' -i, --image_path, sets the path to the image to be predicted. Default=''

Command line:

$ python3 predict_nick.py -m pred --gpu 0 -r <path to model/model.ckpt> -i ../misc/nyu_example.png 

Run a specific model:

$ python3 predict_cv.py -r ../models/NYU_FCRN-checkpoint/NYU_FCRN.ckpt -i ../misc/drone_indoor.mp4

$ python3 predict_cv.py -r output/fcrn/kitti_continuous/all_px/berhu/2018-06-29_17-59-58/restore/model.fcrn ../misc/outdoor_dubai_city.mp4

Detects and lists the available models:

$ python3 predict_cv.py -i ../misc/indoor_drone.mp4 --gpu 0
$ python3 predict_cv.py -i ../misc/outdoor_dubai_city.mp4 --gpu 0

Encode Video:

$ ffmpeg -r 30 -f image2 -s 304x288 -i frame%06d.png -i pred%06d.png -vcodec libx264 -crf 25  -pix_fmt yuv420p ../test.mp4

Dependencies:

1.1) Gstreamer:

$ sudo apt-get install libgstreamer1.0-0 gstreamer1.0-plugins-base gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav gstreamer1.0-doc gstreamer1.0-tools

1.2) ffmpeg:

$ sudo apt install ffmpeg

1.3) Grant access to user for using video devices:

$ grep video /etc/group
$ sudo usermod -a -G video olorin
$ sudo chmod 777 /dev/video0

Dependencies:

$ sudo apt-get install libpng++-dev

Compilation:

$ cd /media/nicolas/nicolas_seagate/datasets/kitti/depth/depth_prediction/depth_devkit/devkit/cpp
sh make.sh

Run:

$ ./evaluation/kitti_depth_prediction_devkit/cpp/evaluate_depth output/tmp/gt/ output/tmp/pred/

Fixes

1. Ubuntu 17.04 libpng12.so.0: cannot open shared object file #95

$ wget -q -O /tmp/libpng12.deb http://mirrors.kernel.org/ubuntu/pool/main/libp/libpng/libpng12-0_1.2.54-1ubuntu1_amd64.deb   && sudo dpkg -i /tmp/libpng12.deb   && rm /tmp/libpng12.deb

Monodepth Evaluation Code:

https://github.com/mrharicot/monodepth/blob/master/utils/evaluate_kitti.py

To evaluate run:

$ python utils/evaluate_kitti.py --split kitti --predicted_disp_path ~/tmp/my_model/disparities.npy \
--gt_path ~/data/KITTI/

• Upload code

• Add Youtube Videos

• Write README.md with framework descriptions

• Change arXiv link to official link

• Change bibtex for the official citation

• Describle the steps for the Densification Framework