Comments (4)
Initializing target depth with zero matrix may cause a performance decay. By the way, unfortunately, if add_geo is turned off, the channels in the network will change and the pretrained model will not be loaded properly, so the network needs to be retrained. If there are initial camera poses, you can run DPSNet or other MVS methods to get initial depth maps. If no camera poses are given, I suggest you to run DeMoN to generate initial camera poses and depths.
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Did you generate inital depth by your depthnet without add_geo? Could you provide a pretrained model without geo?
from deepsfm.
Did you generate inital depth by your depthnet without add_geo? Could you provide a pretrained model without geo?
I didn't do that, and the initial depth is generated via DeMoN. Meanwhile, the structure of depthnet without add_geo is identical to DPSNet, so the pretrained model from DPSNet can be loaded. I didn't retrain the depthnet without geo.
from deepsfm.
Got it, thank you very much.
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Related Issues (16)
- Input data interpretation HOT 3
- minieigen problem HOT 2
- Error: "root:NaN or Inf found in input tensor."
- Is MVS trainset used
- GPU memory cost too much
- Pose matrix multiplication question
- Real time mapping
- ValueError: no unit eigenvector corresponding to eigenvalue 1 HOT 1
- Question about models/PoseNet and iterations HOT 4
- The metrics of depth estimation HOT 4
- Visualization Method HOT 2
- where to download demon_poses.txt and *_demon.npy HOT 3
- lower evaluation metrics using your trained models in google drive? HOT 3
- boost-python-py36 HOT 3
- Chinese verison of paper HOT 8
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