Coded Visual Odometry

Basics

Generate coded images with coded-generator.py. Make sure to edit the camera parameters on line 171 based on your PSFs.

Add new experiments to configopts.py.

Run the trainer: python trainer.py -c <your experiment name>.

Evaluate a model: python tester.py -c <your experiment name> -s <path to checkpoint>

Generate metric depth maps: python generator.py -c <your experiment name> -s <path to checkpoint>

Commands also have a -d or --DATASET flag to set the root of the data folders. Results will be put into each datasets folder next to their coded image.

Datasets

Download the ICL-NUIM dataset. Unzip each into a folder inside a dataset root. For example,

datasets/
	office_traj2/
		depth/
		rgb/
	office_traj3/
		...
	living_traj0/
		...

Edit DatasetName in configopts.py to change what datasets are available. Currently, those datasets must follow the ICL format; however, any dataset with metric depth maps available work. In coded-generator.py call Camera.process_folder with various parameters depending on the folder structure of units of the dataset depth. In data.py, make a new torch.utils.data.Dataset class corresponding to the structure. Then, use that class when loading datasets in the trainer/tester/generator files.