Comments (1)
Yes we didn’t experiment with these, I’d like to try it out though. The issue with 360 dynamic scene capture is that you can presumably not make correspondences across 180 degrees of rotation (e.g., that will be too long in time, so your deforming object has probably deformed a lot), so its unlikely that you could pause the scene and then rotate entirely around it. although maybe with the followup work on on neural trajectory fields this becomes possible. Also, for objects that stay near a rest pose, a canonical approach might be more suitable for 360 rotation.
from neural-scene-flow-fields.
Related Issues (20)
- Coordinate System Operations HOT 3
- error when run the evaluation.py with trained model HOT 3
- Number of training images on Nivida dynamic dataset HOT 1
- Does default config match the implementation detail of the paper
- Evaluation on broom and curl dataset HOT 1
- MiDaS depth prediction -- inverse depth? HOT 1
- RuntimeError: stack expects each tensor to be equal size & AttributeError: 'NoneType' object has no attribute 'shape' HOT 1
- urlopen error [Errno 111] HOT 1
- sceneflow visualization
- What's the minimum system requirements for running the inference?
- How would you recommend adapting NSFF to non-forward facing scenes? HOT 1
- Evaluation Set HOT 2
- Singularly in NDC2Euclidean
- Faster Training.
- Question about Least Kinetic Motion Prior HOT 1
- Implementation different from paper? HOT 2
- Question about softsplat
- How to get the Motion mask?
- poses_bounds.npy only contains poses from one camera HOT 1
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from neural-scene-flow-fields.