NVIDIAGameWorks / NVIDIAGameWorks/kaolin
Encoder design in DMTet
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Description
Hi, Great work. Benefit a lot from kaolin!
I've learned the paper "DMTet" which describes PVCNN as the input encoder. However, I only find "MLP + positional encoding" in kaolin implementation.
So, how could this implementation ensure that the point embedding could capture the local/whole geometry information if there is only point-wise "positional encoding"? (PVCNN could do this, but i 'm not sure if Positional encoding from Nerf could do this)
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Research direction
No file or test is named. Start by locating Kaolin's DMTet implementation and tracing the encoder described in the issue, then compare it with the DMTet paper's PVCNN description. Done means providing a clear explanation of how the implementation's MLP and positional encoding relate to local and whole-geometry information.
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Assessment
- Tech stack
- python, pytorch
- Domain
- computer-graphics, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100