After further research I understand that although we have a 3d volume in an RGB image, we are still doing 2d convolution. The convolution dimensionality refers to the filter dimensions, so in doing 2d convolution on an RGB image we are actually convolving each filter by each channel. In the case of 3d convolution we would alternatively be convolving all 3 channels at the same time by each 3d filter.
Is this accurate?
In the case of 2d convolution of grayscale images, is there a difference between using a batch of say 100 images and using an image of “100 channels”? (besides losing the flexibility of mixing images between batches)