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Neural reconstruction of transparent objects
Congratulations for acceptance of ICCV 2023! Thank you for your great contribution on transparent reconstruction, and thanks for your team work on a series of surface reconstruction following NeuS. I am interested in your work.
For this NeTO reconstruction work, I am confused about the file of "screen_point.npy".
I would like to know:
Furthermore, how do you construct your dataset? I am still not clear about manipulation details after reading your main paper and supplementary material.
Thank you for your kind reply!
Hello author, I am interested in your research direction and would like to compare your code results with my experiment, but I am not sure how to use your code to train my dataset. I saw that at the end of the "datao. py" file, it seemed to generate a "screen_point. npy" file, but when I set my own file path, I found:
ret, corners = cv2.findChessboardCorners(gray, (w, h), flags=cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_FAST_CHECK + cv2.CALIB_CB_NORMALIZE_IMAGE)
This command line did not output the correct result. Can you provide specific instructions on how to operate?
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