Towards Continual, Online, Unsupervised Depth

Introduction

This is the source code for the paper Towards Continual, Online, Unsupervised Depth. This code is for stereo-based depth estimation.

Manuscript is available here.

The SfM-based depth estimation is also available at here.

Requirements

• PyTorch
• Torchvision
• NumPy
• Matplotlib
• OpenCV
• Torchvision
• Pandas

Data Preparation

Download the raw KITTI dataset, the Virtual KITTI RGB, the KITTI test dataset, and Virtual KITTI depth. Extract data to appropriate locations. Saving SSD is encouraged but not required.

Testing

Run

python script_evaluate.py

to display results in console. Run

python script_test_directory.py

to do the evaluations again.

Online Training

Set paths in the options/online_train_options.py file. Then run

python script_online_train.py

The online-trained models (for a single epoch only) will be saved in the trained_models directory. Intermediate results will be saved in the qual_dmaps directory.

Pre-Training

Set paths in the options/pretrain_options.py file. Then run

python pretrain.py

The pre-trained models should be saved in the directory trained_models/pretrained_models/.

Results

Check this video for qualitative results.

The Absolute Relative metric is shown in the following table.

Training Dataset	Approach	Current Dataset	Other Dataset
KITTI	Fine-tuning	0.1920	0.1980
KITTI	Proposed	0.1825	0.1660
VKITTI	Fine-tuning	0.1991	0.2090
VKITTI	Proposed	0.1653	0.1770

See the following figure for comparison.