Self-Supervised Monocular Depth Estimation with Self-Reference Distillation and Disparity Offset Refinement

Paper link: https://arxiv.org/abs/2302.09789

⚙️ Setup

conda create -n srd python=3.7
conda activate srd
conda install pytorch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1 -c pytorch
pip install -r requirements.txt

💾 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

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

Monocular training:

Single GPU:

train.py line9

from trainer_single_gpu import Trainer

CUDA_VISIBLE_DEVICES=0 python train.py --model_name mono_srd

Distributed training:

train.py line9

from trainer import Trainer

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 train.py --model_name mono_srd 

📊 KITTI evaluation

To prepare the ground truth depth maps run:

python export_gt_depth.py --data_path kitti_data --split eigen
python export_gt_depth.py --data_path kitti_data --split eigen_benchmark

...assuming that you have placed the KITTI dataset in the default location of ./kitti_data/.

The following example command evaluates the epoch 19 weights of a model named mono_model:

python evaluate_depth.py --load_weights_folder ./save_models/mono_model/models/weights_19/ --eval_mono