Ask for advice on testing the method on our own data (without ground truth)
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 20/100
- Issue type
- Documentation
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python
- Domain
- computer-vision
Research direction
Start by reviewing the repository's usage instructions and the normal-integration entry point, then trace how normal_map.png, mask.png, and K.txt are consumed. The issue asks whether testing on data without ground-truth normals or depth is supported, so done would be documented guidance or a confirmed evaluation procedure; no specific file or test is named.
Written by the indexing model from the issue text.
Description
Hi, thank you very much for sharing this excellent work and the open-source code!
I am currently trying to test the method on my own data, and I would like to ask for your advice on whether and how this is supported.
Specifically, for each sample I only have:
- normal_map.png (estimated surface normal map)
- mask.png (valid region mask)
- K.txt (camera intrinsic matrix)
I do not have ground-truth normal maps or depth maps for evaluation.
I would greatly appreciate any guidance or pointers.
Thank you again for making this work publicly available!
- Dominant language
- Python
- Stars
- 6
- Forks
- 1
- PR merge metrics
- No merged PRs in 30d
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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