Comments (4)
Hi, this could be expected since the model is not very robust to inference resolution currently. I think the models should be able to handle higher resolutions like ~512x1024, but even higher might be problematic. In practice you might need to tune the inference size to get the optimal performance for your data.
from unimatch.
Thanks for your reply. I'm a newbie in this field. I recently find many research generate depth label by optical flow. I never see any mention about this kind of situation. In tradition, is optical flow model usually sensitive to resolution?
from unimatch.
Yes, this is a common issue.
from unimatch.
Thanks!
from unimatch.
Related Issues (20)
- FEATURE REQUEST: Support 'depth' task using two images with different Intrinsics. HOT 5
- About weights HOT 2
- is this method suitable for panoramas which is about up and down?? HOT 1
- nice work! and plan to live camera gui? HOT 2
- Resetting hidden state during refinement HOT 2
- Running in Windows? HOT 1
- Why the value of optical flow is opposite? HOT 1
- gmstereo_scale1 vs gmstereo_scale2 Inference speed HOT 1
- Decoding RGB Optical Flow for Motion Information Extraction in Video Action Recognition HOT 3
- The optical flow between two identical images looks like random noise. Is this normal? HOT 2
- Request for new weights HOT 1
- gmstereo_scale1_train.sh HOT 1
- The problem of optical flow prediction HOT 1
- unimatch.py line 257 HOT 1
- [GMflow] What do the ’train_dataset ‘ equation coefficients mean when stage=sintel? HOT 1
- Question about model and model_without_ddp HOT 2
- loss curve HOT 2
- Question on upsampling during training. HOT 4
- tartan air dataset HOT 5
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from unimatch.