frames.txt              # meta data about number of frames, image resolution and timestamps for each frame
color_full/             # extracted frames in the original resolution
color_down/             # extracted frames in the resolution for disparity estimation 
color_down_png/      
color_flow/             # extracted frames in the resolution for flow estimation
flow_list.json          # indices of frame pairs to finetune the model with
flow/                   # optical flow 
mask/                   # mask of consistent flow estimation between frame pairs.
vis_flow/               # optical flow visualization. Green regions contain inconsistent flow. 
vis_flow_warped/        # visualzing flow accuracy by warping one frame to another using the estimated flow. e.g., frame_000000_000032_warped.png warps frame_000032 to frame_000000.
depth_${model_type}/    # initial disparity estimation using the original monocular depth model before test-time training
R_hierarchical2_${model_type}/ 
    flow_list_0.20.json                 # indices of frame pairs passing overlap ratio test of threshold 0.2. Same content as ../flow_list.json.
    videos/                             # video visualization of results 
    B0.1_R1.0_PL1-0_LR0.0004_BS4_Oadam/
        checkpoints/                    # checkpoint after each epoch
        depth/                          # final disparity map results after finishing test-time training
        eval/                           # intermediate losses and disparity maps after each epoch 
        tensorboard/                    # tensorboard log for the test-time training process

@inproceedings{kopf2021rcvd,
 title={Robust Consistent Video Depth Estimation},
 author={Kopf, Johannes and Rong, Xuejian and Huang, Jia-Bin},
 year={2021},
 booktitle=IEEE/CVF Conference on Computer Vision and Pattern Recognition
}