Can 3D image like data use this preprocessing layer?

[xuzhang5788]

Up to know, most of the data preprocessing methods are for 2D images or tabular dataframe. But my dataset is about 3D images. I have to do the data preprocessing outside keras. Another problem is my dataset is too big to load into my memory, so I used .h5 file to load my data into the memory batch by batch. Therefore, I can't do sklearn's data preprocessing, such as standardization, normalization, etc. I am not sure if you will deploy more api for 3D data. For example, 3D data augmentation.

[bhack]

Can we specify this? Cause sometimes 3d data augmentation means also synthetic datasets. I.e. 3d dataset where you need to augment inside a minimal 3d engine (opengl, vulkan) for camera, lights and objects parameters.
I don't think that currently we have a minimal scenegraph to drive.
In this case is it plausible roadmap to have some keras interface over https://github.com/tensorflow/graphics?

[xuzhang5788]

@bhack
My data format is like (nb_channels, x, y, z). They are not real images, but on each mesh note, there is a value, look like density cloud. So I want to do data augmentation similar to 2D image data. I want to borrow the concept of the 2D image classification to solve my own project.
Now I rotated my original data several times and save them. Finally, I combined those synthetic datasets with the original one to become one training dataset. If keras can do in-place 3D data augmentation like a 2D ImageDataGenerator(), it can reduce the memory useage and save a lot of data preprocessing time.
Please let me know if I didn't explain my question clearly. Thanks.