Abstract: Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Usually, this GT is acquired at training time through a calibrated multi-modal suite of sensors. However, also using only a monocular system at training time is cheaper and more scalable. This is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision. We compensate the SfM self-supervision limitations by leveraging virtual-world images with accurate semantic and depth supervision and addressing the virtual-to-real domain gap. Our MonoDEVSNet outperforms previous MDE CNNs trained on monocular and even stereo sequences.
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This is an official PyTorch implementation of Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision.
IEEE Transactions on Intelligent Transportation Systems - 2021 (link)
Video Presentation On Monocular Depth Estimation: (1) MonoDEVS ; (2) Multi-modal Co-training by Antonio M. López at 3rd 3D-DLAD @IV'2021
Akhil Gurram, Ahmet Faruk Tuna, Fengyi Shen, Onay Urfalioglu, Antonio M. López.
Clone this github repository:
git clone https://github.com/HMRC-AEL/MonoDEVSNet.git
cd MonoDEVSNetCreate Environment using pip3
pip3 install -r requirementsCreate conda environment based on Conda distribution. All dependencies are in MonoDEVSNet_env.yaml file in configs folder.
conda env create -f configs/MonoDEVSNet_env.yamlEnvironment activation/deactivation
conda activate MonoDEVSNet
conda deactivateWe run our experiments using PyTorch >= 1.5.0, Torchvision>=0.8.2, CUDA>=10.2, Python>=3.7.x with batch_size=12
Prepare monodepth2 framework
Clone monodepth2 repository with minor corrections
cd utils
chmod +x prepare_monodepth2_framework.sh
./prepare_monodepth2_framework.sh
cd ..Prepare KITTI / Virtual KITTI datasets
You can download the entire raw KITTI dataset by running:
wget -i monodepth2/splits/kitti_archives_to_download.txt -P <downloaded_K_folder_path/>
cd <downloaded_K_folder_path>
unzip "*.zip"You can download the entire Virtual KITTI 1.3 / 2.0 into a folder
cd <downloaded_vk_folder_path>
tar -xvf vkitti_2.0.3_rgb.tar
tar -xvf vkitti_2.0.3_depth.tar
tar -xvf vkitti_2.0.3_classSegmentation.tarMonocular Depth Estimation through LiDAR Supervision and Real-world SfM Self-Supervision.
In this variant, we replace the supervision coming from the virtual data to supervision coming from LiDAR
data, this is why we use the term MonoDELSNet instead of MonoDEVSNet.
By default models and tensorboard event files are saved to ~/tmp/<model_name>. This can be changed with the --log_dir flag.
Set pretrained model path in the config file (path) (if necessary download models from link)
# MonoDEVSNet
cd <MonoDEVSNet_base_path>
python3 monodevsnet_trainer.py --png --use_dc --use_le --use_ms --version <an_extension_to_model_name> \
--models_fcn_name encoder <HRNet/ResNet/DenseNet> --num_layers <model_extension_num> \
--real_dataset kitti --syn_dataset <vk_1.0/vk_2.0> --real_data_path <path_to_real_dataset_folder> --syn_data_path <path_to_synthetic_dataset_folder>To evaluate MonoDEVSNet/MonoDELSNet models, provide the model/weights folder path and details in the command line arguments.
To run evaluation script on KITTI Eigen split with original depth maps extracted from raw LiDAR sensor.
python3 evaluation.py --png --dataset kitti --batch_size 1 \
--models_fcn_name encoder <HRNet/ResNet/DenseNet> --num_layers <model_extension_num> \
--real_data_path <KITTI_RAW_Dataset_base_folder_path> --load_weights_folder <path_to_MonoDEVSNet_models> \
[--version <add_extension_to_save_the_file(rgbs/predicted_depth_maps)>](optional)To run evaluation script on KITTI Eigen split with improved (annotated) depth maps.
python3 evaluation.py --png --dataset kitti_depth --batch_size 1 --do_kb_crop \
--models_fcn_name encoder <HRNet/ResNet/DenseNet> --num_layers <model_extension_num> \
--real_data_path <KITTI_RAW_Dataset_base_folder_path> --load_weights_folder <path_to_MonoDELSNet_models> \
[--version <add_extension_to_save_the_file(rgbs/predicted_depth_maps)>](optional)To run evaluation script on any images
python3 evaluation.py --dataset any --models_fcn_name encoder <HRNet/ResNet/DenseNet> --num_layers <model_extension_num> \
--image_folder_path <path_to_image_dir> \
--load_weights_folder <path_to_MonoDEVSNet_models> \ Download available MonoDEVSNet / MonoDELSNet pretrained models from the link and place them under MonoDEVSNet/models folder.
Rename the each MODEL folder name, same as their config-filename.
Now MonoDEVSNet framework can be trained with different network architecture. (HRNet, ResNet, DenseNet)
For more pretrained models using ResNet: {18, 152}, DenseNet: {121, 161}, HRNet: {18, 32, 48} - link
| Model | Encoder | abs-rel | rms | Link | Comments |
|---|---|---|---|---|---|
| MonoDELSNet | ResNet 18 | 0.0614 | 2.2786 | Download | |
| MonoDELSNet | HRNet W48 | 0.0526 | 1.9596 | Download | The Best Model |
To visualize the MonoDEVSNet results, run
python3 utils/load_eval_output.py --file_path <PATH_TO_SAVED/DOWNLOADED_FILE>| Model | Encoder | Virtual dataset | Link |
|---|---|---|---|
| MonoDEVSNet | HRNet W48 | vK 1.0 | Download |
| MonoDEVSNet | HRNet W48 | vK 2.0 | Download |
monodepth2: Digging into Self-Supervised Monocular Depth Prediction
mono-uncertainty: On the uncertainty of self-supervised monocular depth estimation
HRNet-Image-Classification: High-resolution networks (HRNets) for Image classification
DenseNet: Densely Connected Convolutional Networks
Lately verified our training and inference scripts with newer python packages: torch==1.9.0, torchvision==0.10.0, CUDA=11.3, python==3.8.10, numpy==1.21.0 with batch_size=12
The source code is released under the MIT license.
If you want to cite the framework feel free to use this preprint citation while we await publication:
Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision.
Akhil Gurram, Ahmet Faruk Tuna, Fengyi Shen, Onay Urfalioglu, Antonio M. López
@ARTICLE{9565825,
author={Gurram, Akhil and Tuna, Ahmet Faruk and Shen, Fengyi and Urfalioglu, Onay and López, Antonio M.},
journal={IEEE Transactions on Intelligent Transportation Systems},
title={Monocular Depth Estimation Through Virtual-World Supervision and Real-World SfM Self-Supervision},
year={2021},
volume={},
number={},
pages={1-14},
doi={10.1109/TITS.2021.3117059}}Contact: akhil.gurram@e-campus.uab.cat or akhilgurram.ai@gmail.com