Point cloud from depth
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Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Documentation
- Clarity
- Needs clarification
- Activity status
- Quiet
- Tech stack
- python
- Domain
- computer-vision
Research direction
The issue does not name a source file, test, or implementation entry point. Start by reading the paper's 2.5D point-cloud section and the project's depth-processing documentation or examples, then determine whether the requested clarification belongs in the documentation. Done means explaining how predicted depth and camera intrinsics produce a 3D point cloud and clarifying the phrase "reasonable guess of a range."
Written by the indexing model from the issue text.
Description
Hey! Thanks for this amazing work!
How can I get 3D point cloud from depth?
I know intrinsics of a camera and when I project pixels based on intrinsics and predicted depth(not inverse) I get a flat point cloud.
In your paper there is information how to get 2.5D point cloud, but this information isn't clear for me.
What does "reasonable guess of a range" phrase mean?
Thanks!
Below some examples of generated 3D point cloud from samples of Nersemble Dataset.
- Dominant language
- Python
- Stars
- 397
- Forks
- 34
- PR merge metrics
- No merged PRs in 30d
Contributor guide
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