Comments (5)
In my series of experiments, the face selfie videos perform the best and most stable.
from hypernerf.
In my series of experiments, the face selfie videos perform the best and most stable.
Thank you for your reply,my data set is the endoscope data set, I don't know whether it is applicable, and is there any requirement for FPS in video segmentation? My data camera moves slowly, will it affect the quality of training?
from hypernerf.
When preparing datasets for HyperNeRF, it captures some frames (default: 100 frames) from the video at equal intervals, then reconstructs on the basis of these frames.
So I think the key to good reconstruction performance is not high frame rate or camera moving speed, but enough pictures from different observation angles.
from hypernerf.
Thank you for your answer. I see what you mean. I think you're right. My data is different from the face data. It is not a relatively fixed rotating object, but a video image taken in the abdominal cavity. Maybe my video is not suitable for this method, so I will find another solution to see if it can be solved. Thanks again for your reply!
from hypernerf.
Thank you for your answer. I see what you mean. I think you're right. My data is different from the face data. It is not a relatively fixed rotating object, but a video image taken in the abdominal cavity. Maybe my video is not suitable for this method, so I will find another solution to see if it can be solved. Thanks again for your reply!
You're welcome. Wish you success!
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Related Issues (20)
- LPIPS metric computation HOT 1
- Hyper-Space Template
- Inconsistent shapes between value and initializer for parameter "kernel" in "/warp_field/trunk/hidden_0" HOT 1
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- Some tips on collecting selfie video for hypernerf? HOT 5
- TypeError: broadcast_to requires ndarray or scalar arguments, got <class 'list'> at position 0. HOT 1
- ValueError: Non-hashable static arguments are not supported HOT 1
- Problem with Geman-McClure loss implementation HOT 2
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- how to rerender video just like Project Page showing
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- using gpu memory but not computation
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- viewer
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