why do we need K=32 Gaussians for each active voxel? what is the meaning behind this K=32 Gaussians?
Open
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 13.7k
- Forks
- 1.3k
- PR merge metrics
- No merged PRs in 30d
Description
Hello, Thank you for your great work.
I have a question when I read your paper, that is about K=32 Gaussians for each active voxel. Coud you please give us some detailed explanations?
could we give like K=1 gaussian or k=64 gaussians?
Thank you in advance
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 names no repository file, test, or entry point; start by reading the paper's explanation of K=32 and locating the corresponding Gaussian representation in the TRELLIS code. Done means documenting why K=32 is used and whether K=1 or K=64 is supported or would require changes.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-graphics, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100