Comments (4)
Hi,
Your errors seem quite low, it looks like the training went ok.
I do not visualize the depth directly but the inverse of it (or disparity).
If you try to save 1.0 / pred_depth you should see a reasonable image.
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ok, let me try that
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ok, I got the disparity images which look reasonable. By the way in the function def scale_pyramid(self, img, num_scales): I had to cast the nh and nw as tensor elements otherwise the code was giving errors,
nh = tf.cast(nh, tf.int32)
nw = tf.cast(nw, tf.int32)
scaled_imgs.append(tf.image.resize_area(img, [nh, nw]))
The above change worked for me. I am using Python3 though and I guess your code is based on Python2.
I had one more question - your encoder for vgg as I see is pretty straightforward but for the decoder part is there any related article or link that I can read upon to get a better feel of it and how the disparities are getting output at different levels.
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The code was written for python2, I'll need to update at some point to make sure it works for both.
The decoder, is inspired by dispnet and flownet from Thomas' Brox group.
https://arxiv.org/abs/1504.06852
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Related Issues (20)
- Test results not good after training on custom data HOT 6
- disparity map error HOT 1
- Training without CUDA HOT 1
- Question About Disparity Smoothness Loss
- Relative paths don't work for checkpoint_path
- Total parameters
- Can we use any camera for depth estimation ?
- About the kitti weight in kitti_archives_to_download.txt HOT 2
- Load ImageNet weights for ResNet50 HOT 2
- How to create my own dataset? HOT 2
- Non Linearity on Outputted Disparity.
- Run on windows
- How is the uncertainty measured?
- world coordinates
- testing simple.py has bad result HOT 3
- Difference between upconv and iconv
- Test - why don't you evaluate the loss function ?
- Calculating C1 and C2 error for Make3D dataset HOT 1
- How to load the pre-training model
- Is that possble to use the algorithm in Edge devices
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