Comments (5)
Yes, the output is a floating-point value. Each output map is scaled by an unknown factor relative to the ground truth (i.e., it's not in units of meters or anything like that).
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The model estimates depth up to an unknown scale parameter, so the units themselves are not that meaningful. The error metrics we use for evaluation measure the accuracy of the depth map up to scale. This is a consequence of the training data (multi-view stereo) also having a scale ambiguity.
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Hi fcole,
Do you mean that a depth map predicted by the pre-trained model is scaled by an unknown factor, in comparison with the "depth ground truth " ?
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Hi, is the depth image predicted by the network a 32-bit continous floating-point image? Or is it just an 8-bit image?
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Thanks for your reply. I found that such scaling factor is correlated with the normalization of depth ground truth (i.e. normalized from 1 to 3 or from 1 to 10 meters) when I train a model. The factor is also increased with the enhancement of training epoch.
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Related Issues (20)
- typo in instruction HOT 1
- Images Downscaled HOT 1
- CPU Support HOT 3
- Camera pose and intrinsics estimation at inference time HOT 2
- Colmap Patch matching settings HOT 2
- Code to generate depth maps from Youtube videos HOT 1
- How to generate HDF5 data?
- RuntimeError: unexpected EOF, expected 109330 more bytes. The file might be corrupted.
- Clarifications on training pipeline HOT 2
- Running on custom videos HOT 3
- thank you for your work~when do you offer the training code? HOT 12
- Could I get more detail about object insertion from depth-based visual effect part?
- training data
- will you share the MannequinChallenge Dataset? HOT 2
- Would you like to share the initial depth estimation methods? HOT 3
- Some of the video in the datasets become unavailable HOT 5
- Inference Performance HOT 2
- How to get the camera intrinsic parameters from the original YouTube wild videos? HOT 1
- Inference of the multi-view model - guidance required HOT 6
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