Tips for handling the code for monocular dataset.

Hi,
Thanks for sharing such awesome work and nicely organized code!
I want to study this large-scale code as a baseline network (framework) in detail, and then explore ideas for casually captured monocular videos.
I have a few questions as follows:

1. This large code seems to be implemented based on PyTorch Lightning. Was this code developed from scratch? If so, could you provide some tips/guidelines or links to help me understand the overall flow of this code in detail? Any brief explanation of an outline, a rule, or how to debug for organizing this large-scale code would greatly help me in my studies.

2. If I want to test HyperReel on the Neural 3D Video dataset with a monocular setting (e.g., only using camera1 among 20 cameras for 50 frames or all 300 frames at once), how can I modify a config or a YAML file associated with "scripts/run_one_n3d.sh"?

3. If my own monocular video (forward-facing dataset) is provided as extracted frames (.png, not video .mp4) with bose_bound.npy, how can I handle this dataset in this code structure for training HyperReel (any suggestion for referring to YAML/Config file)? Do I have to convert the monocular video into a .mp4 format?

4. What does "hold_out" mean? (hold_out vs. no_hold_out)

Thank you very much!

Start with scripts/run_one_n3d.sh and trace the associated YAML or config files to document the execution flow and dataset settings. Then inspect how extracted frames, bose_bound.npy, monocular camera selection, frame counts, and hold_out are handled. Done means providing accurate usage guidance or documentation for each of the four questions; the issue has no recent answer.

Written by the indexing model from the issue text.