Comments (2)
@fangchangma can you please provide us the instructions that we can follow to make inferences using your pretrained models?
@hello7623 or did you already figure that out? If yes, could you please tell how did you do that?
from self-supervised-depth-completion.
My findings, while looking around in the code and trying around:
- there is no plain inference method.
- You cannot use the pretrained model on only images. As far as I understand, the pretrained model uses the input 'd' and 'rgb', which means both sparse depth and rgb image data. Therefore you won't get around training the model yourself if you have no depth data.
from self-supervised-depth-completion.
Related Issues (20)
- Error while loading "calib_cam_to_cam.txt" - can not reshape the array.
- question about depth-estimation results HOT 2
- What is the network used for single d?
- Why I can't get the result when using the trained model you provided?
- How can I get the result in your paper?
- About extracting trained model HOT 2
- Clip output in model.py
- colorize the depth map HOT 1
- some problem about photometric_loss
- Use your pretrained model: GPU run out of memory. 8.95 gb already allocated
- Save output depth map HOT 1
- dataset extracting
- Training doesn't converge HOT 4
- silog error measurement
- Running Error in train mode sparse+photo HOT 1
- To much warning. HOT 2
- Use Stereo Pair Instead of Temporal Pair for Self-Supervised Training?
- The result cannot be reproduced
- Some questions about the details of the code
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from self-supervised-depth-completion.