What is the EDGS initialization method different from adopting a dense point cloud using the MVS method directly?
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 742
- Forks
- 59
- PR merge metrics
- No merged PRs in 30d
Description
Thanks to the author for his excellent work! There is related work doing 3DGS initialization using dense point clouds generated by the MVS method, and your approach uses Roma feature matching and triangulation to reconstruct denser point clouds by sampling portions of all images.
I was wondering if there is any difference between your method and using MVS initialization?
Is it an improvement in efficiency or is the reconstructed point cloud in 3D space of higher quality?
How long does it take to reconstruct a dense point cloud using your method?
Contributor guide
No contributing guide indexed for this repository
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.
Research direction
The issue does not name files, tests, or entry points. Start by comparing EDGS's Roma feature-matching and triangulation initialization with dense point clouds produced by MVS, then examine reconstruction time and point-cloud quality against direct MVS initialization. Done means documenting the differences and reporting the relevant timing or quality observations.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, pytorch
- Domain
- computer-vision
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100