Comments (4)
The temporal difference strategy works like this: Given a fixed-position camera, compare pixel values over time to detect motion, and then prioritize training with those pixels that change value. If the camera is moving between different timesteps, then you can't discover and prioritize motion by doing pixel-wise temporal differencing because all the pixels will exhibit camera motion.
from k-planes.
Thanks your reply. You may understand me wrong, I understand how IST is calculated, I want to ask why IST does not apply to monocular videos (only one fixed camera)?
from k-planes.
Ah, usually when people say "monocular videos" in the context of radiance field modeling, they mean a single camera that moves around over time. If there is only one camera and it doesn't move, then you don't get enough information for a volumetric reconstruction (at least, not without relying on very strong depth priors).
from k-planes.
Thank you very much! By the way, help me see see issue #38 (comment).
from k-planes.
Related Issues (20)
- Please provide a set of parameters to reproduce the results on the lego dataset HOT 2
- Training on Custom Data HOT 4
- Visualization/Evaluation ERROR HOT 4
- Mismatch in total variation loss between description in paper and the implementation HOT 2
- OOM issue HOT 3
- Question about result of DyNeRF dataset : a difference between the results in the paper and my results HOT 6
- Bad performance on other dataset. HOT 4
- Questions about planes HOT 1
- Performance on TanksandTemple dataset HOT 2
- Model size HOT 5
- Replicating Fig 8 from the paper HOT 2
- IndexError at "video_datasets.py" HOT 1
- why is ndc_far=2.6 and not 1?
- About average_poses function?
- Query Regarding 'bds.npy' File in K-Planes Dataset HOT 1
- About Bilinear interpolation?
- Inconsistent Results Despite Setting Random Seed HOT 2
- used coordinate system in Phototourism
- Image Height and Width Flipped for D-NeRF scenes
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