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fangchangma / self-supervised-depth-completion

ICRA 2019 "Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera"
MIT License
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some problem about photometric_loss #44

Open zitao359 opened 4 years ago

zitao359 commented 4 years ago

Hi,

Thanks for open-sourcing this great piece of work!

I am trying to implement your code and faced some problem，I see that in the main.py file is calculating loss2 (photometric_loss), you use rgbcurr (the image of the current frame), warped_ (the image of the neighboring frame predicted by the current frame image), and proofread through the mask to calculate the photometric_loss . So my question is

  1. Is my understanding of rgbcurr and warped_ correct? If not, I hope to get your corrections.

  2. Why use the current frame and predicted neighboring frame images to calculate photometric_loss, instead of using the current frame and predicted current frame to calculate photometric_loss.

  3. In your paper, I have seen guidance on using RGB images for depth prediction, Is it the only way to calculate the photometric_loss using the RGB guide? If not, I hope you can give me your advice. Do you have any suggestions?

I have just come into contact with this knowledge, and there may be something wrong. I hope you forgive me.

Thanks for the help!