MonoViT

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

MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer arxiv

Chaoqiang Zhao*, Youmin Zhang*, Matteo Poggi, Fabio Tosi, Xianda Guo,Zheng Zhu, Guan Huang, Yang Tang, Stefano Mattoccia

<div class='paper-box'><div class='paper-box-image'><img src='fig/kittiandds.png' alt="sym" width="90%"></div> <div class='paper-box-text' markdown="1">

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

@inproceedings{monovit,
  title={MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer},
  author={Zhao, Chaoqiang and Zhang, Youmin and Poggi, Matteo and Tosi, Fabio and Guo, Xianda and Zhu, Zheng and Huang, Guan and Tang, Yang and Mattoccia, Stefano},
  booktitle={International Conference on 3D Vision},
  year={2022}
}

⚙️ Setup

Assuming a fresh Anaconda distribution, you can install the dependencies with:

pip3 install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0
pip install dominate==2.4.0 Pillow==6.1.0 visdom==0.1.8
pip install tensorboardX==1.4 opencv-python  matplotlib scikit-image
pip3 install mmcv-full==1.3.0 mmsegmentation==0.11.0  
pip install timm einops IPython

We ran our experiments with PyTorch 1.9.0, CUDA 11.1, Python 3.7 and Ubuntu 18.04.

Note that our code is built based on Monodepth2

Results on KITTI

We provide the following options for --model_name:

| --model_name | Training modality | Pretrained? | Model resolution |Abs Rel| Sq Rel| RMSE| RMSE log| delta < 1.25 | delta < 1.25^2 | delta < 1.25^3 | |-----------------------|-------------|------|-----------------|----|----|----|------|--------|--------|--------| | mono_640x192 | Mono | Yes | 640 x 192 | 0.099 |0.708 |4.372| 0.175 |0.900 |0.967| 0.984| | mono+stereo_640x192 | Mono + Stereo | Yes | 640 x 192 | 0.098| 0.683| 4.333| 0.174| 0.904| 0.967| 0.984| | mono_1024x320 | Mono | Yes | 1024 x 320 | 0.096| 0.714| 4.292| 0.172| 0.908| 0.968| 0.984| | mono+stereo_1024x320 | Mono + Stereo | Yes | 1024 x 320 | 0.093 |0.671 |4.202 |0.169 |0.912 |0.969 |0.985| | mono_1280x384 | Mono | Yes | 1280 x 384 | 0.094 |0.682| 4.200| 0.170| 0.912| 0.969| 0.984|

Robustness

| Model | Modality | mCE (%) | mRR (%) | Clean | Bright | Dark | Fog | Frost | Snow | Contrast | Defocus | Glass | Motion | Zoom | Elastic| Quant| Gaussian | Impulse | Shot | ISO | Pixelate | JPEG | | :-- | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | | MonoDepth2_R18| Mono | 100.00 | 84.46 | 0.119 | 0.130 | 0.280 | 0.155 | 0.277 | 0.511 | 0.187 | 0.244 | 0.242 | 0.216 | 0.201 | 0.129 | 0.193 | 0.384 | 0.389 | 0.340 | 0.388 | 0.145 | 0.196 | | MonoDepth2_R18+nopt | Mono | 119.75 | 82.50 | 0.144 | 0.183 | 0.343 | 0.311 | 0.312 | 0.399 | 0.416 | 0.254 | 0.232 | 0.199 | 0.207 | 0.148 | 0.212 | 0.441 | 0.452 | 0.402 | 0.453 | 0.153 | 0.171 | | MonoDepth2_R18+HR | Mono | 106.06 | 82.44 | 0.114 | 0.129 | 0.376 | 0.155 | 0.271 | 0.582 | 0.214 | 0.393 | 0.257 | 0.230 | 0.232 | 0.123 | 0.215 | 0.326 | 0.352 | 0.317 | 0.344 | 0.138 | 0.198 | | MonoDepth2_R50 | Mono | 113.43 | 80.59 | 0.117 | 0.127 | 0.294 | 0.155 | 0.287 | 0.492 | 0.233 | 0.427 | 0.392 | 0.277 | 0.208 | 0.130 | 0.198 | 0.409 | 0.403 | 0.368 | 0.425 | 0.155 | 0.211 | | MaskOcc | Mono | 104.05 | 82.97 | 0.117 | 0.130 | 0.285 | 0.154 | 0.283 | 0.492 | 0.200 | 0.318 | 0.295 | 0.228 | 0.201 | 0.129 | 0.184 | 0.403 | 0.410 | 0.364 | 0.417 | 0.143 | 0.177 | | DNet_R18 | Mono | 104.71 | 83.34 | 0.118 | 0.128 | 0.264 | 0.156 | 0.317 | 0.504 | 0.209 | 0.348 | 0.320 | 0.242 | 0.215 | 0.131 | 0.189 | 0.362 | 0.366 | 0.326 | 0.357 | 0.145 | 0.190 | | CADepth | Mono | 110.11 | 80.07 | 0.108 | 0.121 | 0.300 | 0.142 | 0.324 | 0.529 | 0.193 | 0.356 | 0.347 | 0.285 | 0.208 | 0.121 | 0.192 | 0.423 | 0.433 | 0.383 | 0.448 | 0.144 | 0.195 | | HR-Depth | Mono | 103.73 | 82.93 | 0.112 | 0.121 | 0.289 | 0.151 | 0.279 | 0.481 | 0.213 | 0.356 | 0.300 | 0.263 | 0.224 | 0.124 | 0.187 | 0.363 | 0.373 | 0.336 | 0.374 | 0.135 | 0.176 | | DIFFNet_HRNet | Mono | 94.96 | 85.41 | 0.102 | 0.111 | 0.222 | 0.131 | 0.199 | 0.352 | 0.161 | 0.513 | 0.330 | 0.280 | 0.197 | 0.114 | 0.165 | 0.292 | 0.266 | 0.255 | 0.270 | 0.135 | 0.202 | | ManyDepth_single | Mono | 105.41 | 83.11 | 0.123 | 0.135 | 0.274 | 0.169 | 0.288 | 0.479 | 0.227 | 0.254 | 0.279 | 0.211 | 0.194 | 0.134 | 0.189 | 0.430 | 0.450 | 0.387 | 0.452 | 0.147 | 0.182 | | FSRE-Depth | Mono | 99.05 | 83.86 | 0.109 | 0.128 | 0.261 | 0.139 | 0.237 | 0.393 | 0.170 | 0.291 | 0.273 | 0.214 | 0.185 | 0.119 | 0.179 | 0.400 | 0.414 | 0.370 | 0.407 | 0.147 | 0.224 | | MonoViT_MPViT | Mono | 79.33 | 89.15 | 0.099 | 0.106 | 0.243 | 0.116 | 0.213 | 0.275 | 0.119 | 0.180 | 0.204 | 0.163 | 0.179 | 0.118 | 0.146 | 0.310 | 0.293 | 0.271 | 0.290 | 0.162 | 0.154 | | MonoViT_MPViT+HR | Mono | 70.79 | 90.67 | 0.090 | 0.097 | 0.221 | 0.113 | 0.217 | 0.253 | 0.113 | 0.146 | 0.159 | 0.144 | 0.175 | 0.098 | 0.138 | 0.267 | 0.246 | 0.236 | 0.246 | 0.135 | 0.145 |

The RoboDepth Challenge Team is evaluating the robustness of different depth estimation algorithms. MonoViT has achieved the outstanding robustness.

💾 KITTI training data

You can download the entire raw KITTI dataset by running:

wget -i splits/kitti_archives_to_download.txt -P kitti_data/

Then unzip with

cd kitti_data
unzip "*.zip"
cd ..

Warning: it weighs about 175GB, so make sure you have enough space to unzip too!

Our default settings expect that you have converted the png images to jpeg with this command, which also deletes the raw KITTI .png files:

find kitti_data/ -name '*.png' | parallel 'convert -quality 92 -sampling-factor 2x2,1x1,1x1 {.}.png {.}.jpg && rm {}'

or you can skip this conversion step and train from raw png files by adding the flag --png when training, at the expense of slower load times.

The above conversion command creates images which match our experiments, where KITTI .png images were converted to .jpg on Ubuntu 16.04 with default chroma subsampling 2x2,1x1,1x1. We found that Ubuntu 18.04 defaults to 2x2,2x2,2x2, which gives different results, hence the explicit parameter in the conversion command.

You can also place the KITTI dataset wherever you like and point towards it with the --data_path flag during training and evaluation.

Splits

The train/test/validation splits are defined in the splits/ folder. By default, the code will train a depth model using Zhou's subset of the standard Eigen split of KITTI, which is designed for monocular training. You can also train a model using the new benchmark split or the odometry split by setting the --split flag.

Custom dataset

You can train on a custom monocular or stereo dataset by writing a new dataloader class which inherits from MonoDataset – see the KITTIDataset class in datasets/kitti_dataset.py for an example.

⏳ Training

PLease download the ImageNet-1K pretrained MPViT model to ./ckpt/.

For training, please download monodepth2, replace the depth network, and revise the setting of the depth network, the optimizer and learning rate according to trainer.py.

Because of the different torch version between MonoViT and Monodepth2, the func transforms.ColorJitter.get_params in dataloader should also be revised to transforms.ColorJitter.

By default models and tensorboard event files are saved to ./tmp/<model_name>. This can be changed with the --log_dir flag.

Monocular training:

python train.py --model_name mono_model --learning_rate 5e-5

Monocular + stereo training:

python train.py --model_name mono+stereo_model --use_stereo --learning_rate 5e-5

GPUs

The code of the Single GPU version can only be run on a single GPU. You can specify which GPU to use with the CUDA_VISIBLE_DEVICES environment variable:

CUDA_VISIBLE_DEVICES=1 python train.py --model_