Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Image names covered in brine, custom datasets and LLFF compatibility #8

Closed
Sazoji opened this issue Aug 1, 2022 · 2 comments
Closed

Comments

@Sazoji
Copy link

Sazoji commented Aug 1, 2022

Your dataset structure follows common LLFF, and I understand that the main issue with custom datasets and LLFF is the lack of file names being presented to the model. I had similar issues with nvdiffrec and a simple list solves any number of memory leaks that happen when loading images/files rejected by colmap.

a "view_imgs.txt" is pretty important I'd think, and I'm glad some of the example datasets use a poses.npy -I do not understand the reasoning to use remove.bg masks, construct datasets with another .db file, and pickle a list of files (instead of a readable .txt) that users might want to edit when making their own sets.

if os.path.exists(os.path.join(mask_dir, "%s-removebg-preview.png"%img_id)):
when I am trying to handle masks, in their own separate folder, would I want them just to be a b/w image out of any number of salient object matting repos, or images specifically from remove.bg of a separate size (to then use bicubic filtering on), only in the alpha, and containing an entire unused image?

There are 0 technological limitations on making a dataset renderable and testable in instant-ngp, meshable in nvdiffrec (and this repo), optimizable in AdaNeRF and R2L, and still be created from a video shot on a phone. If you're planning on making a dataset creation guide, please dont use removebg filenames, dont use new db files, dont use non user-readable lists of files (it takes one extra line to parse a .txt file), and have support for traditional b/w masks.
all that's needed is /images, /masks, imgs.txt, and a poses.npy (pts seems like it's to build a bounding box and isn't in all your example sets)
lowering that barrier allows anyone who knows how to run a script to make datasets, and it's WHY instant-ngp worked, anyone could try it out with ffmpeg and a script. Forks are being made to test datasets made from my colmap2poses script, if a simple colmap2NeROIC script is needed to read colmap data I can make a push with a more forgiving LLFF dataloader and said script.

@zfkuang
Copy link
Contributor

zfkuang commented Aug 1, 2022

Hey Sazoji,

Thanks for the suggestions. We will release scripts and guides for building dataset from Colmap soon.

@zfkuang zfkuang closed this as completed Aug 1, 2022
@zfkuang
Copy link
Contributor

zfkuang commented Aug 5, 2022

The BYOD scripts are updated :)

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants