How can marigold depth completion benefit from this work?
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 15/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python
- Domain
- computer-vision, machine-learning
Research direction
The issue names no files, tests, or entry points, so begin by reading the repository documentation and the implementation related to RGB and sparse-depth inputs. Clarify how the project is expected to support Marigold depth completion and define a concrete, testable outcome before starting work.
Written by the indexing model from the issue text.
Description
Hi,
My use case is based on RGB image and sparse depth input, but now the iteration speed is the bottleneck, how can this function benefits from your work? Thank you
- Dominant language
- Python
- Stars
- 521
- Forks
- 22
- PR merge metrics
- No merged PRs in 30d
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.
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VisualComputingInstitute/diffusion-e2e-ft#20 · 6 comments ·
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