Closed Kasai2020 closed 2 years ago
Hi @Kasai2020, Since depth is used as an additional input to our network i.e. we used RGB-D as an input, we normalize it by a scale of 255.0
. To visualize this depth, we un-normalize and multiply by 255.0. Note that this normalization is different from depth encoding i.e. 16 bit etc. We do this normalization after loading the 16-bit depth. Usually normalization helps with network stability during training. Hope it helps!
Thank you for the in depth and fast reply!
I had a question about how you are preprocessing the depth from the NOCS dataset. In generate_data_nocs.py, you divide the depth (which I believe is in mm) by 255 before writing it as a panoptic datapoint. This is on line 234:
Is this intended? and why 255.0?