Pipeline

Requirements

torchvision 0.12.0, CUDA 10.2, tensorboardX 1.4

If you're using older version of torchvision, you need to:

• be careful with align_corners=True using F.interpolate and F.grid_sample to garentee a good camera trajectory, as these methods has been changed to use align_corners=False by default in later version.
• replace transforms.ColorJitter() with transforms.ColorJitter.get_params().

Dataset

The dataset is available on the SCARED official website.

Date preprocess

ffmpeg -i /path/to/rgb.mp4 -filter:v "crop=1280:1024:0:0" /path/to/crop_rgb.mp4
ffmpeg -i /path/to/crop_rgb.mp4 %6d.jpg

Training

CUDA_VISIBLE_DEVICES=1 python train.py --data_path <path/to/SCARED/> --log_dir <path/to/save/weights>  --batch_size 20 --frames_ids 0 -1 1 -2 2

Validation

CUDA_VISIBLE_DEVICES=1 python evaluate_depth.py --load_weights_folder <weights_path> --eval_mono  --eval_split endovis --data_path </path/to/SCARED> --max_depth 150.0
CUDA_VISIBLE_DEVICES=1 python evaluate_pose.py --data_path <path/to/SCARED/> --eval_split endovis --load_weights_folder <weights_path>

Testing

Model	Abs Rel	Sq Rel	RMSE	RMSE log	Link
Results	0.058	0.452	5.014	0.083	google drive

CUDA_VISIBLE_DEVICES=1 python test_simple.py --model_path <model_path> --image_path <image_path>

visualisation

visualize pose

python visualize_pose.py

visualize 3d reconstruction

python visualize_reconstruction.py --data_path <path/to/dataset> --load_weights_folder <path/to/weight/folder> --eval_mono

Colab Demos

• Colab Demo : Visualise the output of the dataloader. Calculate the depth and pose metrics and visualise trajectory using pretrained weights.

Acknowledgement

Our code is built on top of the implementation of Monodepth2.