Enhancements were made on the loss functions and the network architecture. Check the report for more details.
TRAINING:
- Paste the TrainingAndValData/ Folder with the prepared kitti data in this folder.
- Enter the folder using cd TrainingAndValData/
- Copy SfMLearnerDatatrain.txt and SfMLearnerDatatrain.txt inside ./TrainingAndValData/SfMLearnerData
To train default version, check if the SfMLearner class from SfMLearner.py is imported in train.py
- cd SfmLearner/
- run
python3 train.py
To train the modified version, check if the SfMLearner class from SfMLearner_SSIM.py is imported in train.py and perform the
- cd SfmLearner/
- run
python3 train.py
EVALUATION: The pose evalution data is already downloaded in ./SfMLearner/kitti_eval
Download the raw odometry data from kitti website and paste it inside ./TrainingAndValData
For testing pose :
- first run test_kitti_pose.py to get predictions, choose the appropriate groundtruth and predictions folder
- navigate to cd kitti_eval
- next run eval_pose to get results. Note: in case of running pretrained pose model, change sequence length = 5
For testing depth :
- first run test_kitti_depth.py to get the depth predictions
- navigate to cd kitti_eval
- next run eval_depth to get results.
To visualize depth use the visualize.ipynb notebook in kitti_eval
References for modified architectures: https://github.com/yzcjtr/GeoNet
Link for presentation video: https://www.youtube.com/watch?v=bWV-gceHxxs