Comments (2)
Hi @aj96,
Yes, we believe that using border edge padding produces better results than masking, and your experiment lends some evidence to this. The exact reasons for this, though, are not well studied. If you do more work to understand the benefits and weaknesses of different ways of dealing with 'fly-out' regions I'm sure the wider community would like to hear about it!
Thanks
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I know this was mentioned in this issue: #20 However, it was said that this was only an issue for the stereo case.
I tried manually computing the fly-out mask and replacing the invalid region of the warped image with the actual pixels from the target image. And in some cases, this led to the depth net predicting inf depth at the edges. I realize that this is different from using zero padding when performing the grid sampling. But I would think this would lead to better results, not worse, because by using the fly-out mask, you are not allowing the fly-out pixels to contaminate the loss.
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Hello, I have been experimenting with this question recently, but I encountered some problems when computing the mask. Can I ask how you compute the mask?
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Related Issues (20)
- Network inference time problem
- The requested array has an inhomogeneous shape after 1 dimensions. HOT 5
- Write split file HOT 2
- obtained some very strange depth maps HOT 4
- The difference in the intrinsic matrix affects the results
- Question about image resolution
- question for the Data Preparation
- Why is smooth_loss divided by 2**scale?
- Questions about the meaning of grid in the F.grid_sample function
- the eval file about 'gt_depths.npz'
- Run on Google Colab,
- Run on Google Colab, but out of System RAM
- The new constraint about pose is not useful?
- RuntimeError: CuDNN error: CUDNN_STATUS_SUCCESS HOT 2
- How to obtain reconstructed image and loss for a single demo image
- How to setup already trained computer vision model Ultralytics YOLOv8 with monodepth2 HOT 1
- Questions about pose estimation HOT 1
- Different evaluation criteria for Monodepth2 M and S
- Regarding the patent and license HOT 1
- RuntimeError: The size of tensor a (319) must match the size of tensor b (639) at non-singleton dimension 3
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