lizhenwangt / normalgan Goto Github PK

Great work!
I am tring to get the mesh of my pictures taken by ipad Lidar. https://developer.apple.com/forums/thread/663995.I get the intrinsics here,but I could not get the right mesh, the images in result/cz0,cz1,dz0,dz1 are all incorrect. when I test your data, the result is correct.https://developer.apple.com/forums/thread/663995

First of all, this is very interesting research. Thank you so much for sharing the repository! I am trying to play around with the approach on my own data. Is it possible for you to share your training code?

Hi! Thank you very much for your excellent work. I've got great reconstruction. However, I have a question how to get the depth map corresponding to the RGB picture?

Hi,
Interesting work. The supplementary section mentions that the front-view depth rectification network outputs a 1D rectified depth image and a 1D binary mask of the orthographic view. How does the mask help in learning depth image? Would I get the same results if I train the network without predicting the orthographic mask?

Hi, Wang

I really appreciate your great work!

I want to run NormalGAN on my own KinectV2 data, but met some problems:
The output is so terrible, and it looks even not like a human at all...
I tried to put many frames into NomalGAN, and also did some simple denoising, but the outputs nearly kept the same...(T_T)
My color map: size: 512 * 424, type: CV_8UC3 (24bit);
My depth map: size: 512 * 424, type: CV_16UC1 (16bit);
Here are my maps and output:

Here are the cz0 and cz1:

Your NoramalGAN is so magic and I really want to play it well on my data.
So I hope you can give me some advice. Thanks a lot!

Best wishes

Hello, amazing work, really love it, because i don't have a kinect, can I use the result of sigle view depth estimation methods instead of kinectV2 depth?

how to downing pretrained models and test ?

Hi, Wang

Thank you very much for your excellent work.

I want to run NormalGAN on data from Azure Kinect, does it work for Azure Kinect？

Thank you!