jhornauer / grumodepth Goto Github PK

Thank you for sharing the codes and the models.
While reading the code I have a remark regarding the python file evaluate_supervised.py. Is there a specific reason you are testing separately if opt.grad in lines 204 and 217?
Thanks.

Dear Authors, @jhornauer
Thank you for the insightful paper. I was trying to implement this on a different dataset. In the process of creating a virtual Conda env, when I am trying to install packages from the .yml file around 50% are saying no version available.
Do you know what the reason could be for this?

Thank you,
Shubham

Shouldn't be there a relu activation after the uncertainty estimation in the supervised scenario. Since the network could predict negative uncertainty which is not intuitive; and would result in negative loss. Or is this intended behavior ?

Why does depth estimation layer doesn't have a sigmoid?

Do you have sigmoid after uncertconv layer?

Hi @jhornauer
I am facing an issue when running the generate_maps.py
In line 487, when calling the get_mono_ratio function we are trying to resize the reciprocal of pred_disps to the gt_depths. But the cv2.resize function as defined is not working and throwing an error saying : IndexError: too many indices for array: array is 2-dimensional, but 3 were indexed

In my opinion I found this to be the case because the resize parameters as defined in the function (get_mono_ratio) are not giving the right image dimensions.

Can you please help with the issue.

Thank you,
Shubham

Thank you for sharing the codes. BTW the paper is clear and intuitive.

However, I did not find the results for MS (monocular + stereo supervision) on KITTI dataset in the paper, while in mono-uncertainty paper, the authors provided them. I know this is an additional request, but since usually MS has better depth accuracy than M and S, maybe you can provide its uncertainty results in the supplementary material?

Thank you.

Hi @jhornauer

I had doubts in my understanding regrading what AURG means and what it signifies.

First the paper says that AURG is an estimation if we assume there is no modelling in the system. Here no modelling means, not arranging the uncertainty values in descending order or something else? what does it mean intuitively when we say that higher the value of AURG the better?

Thank you for your time,
Shubham