If I understand your question correctly you want to concatenate two 2D RGB images together and process them at the same time.

You are talking about using conv3d, unless you have a 3d volume as input, I don’t see how it’s going to work.
Merging 2 RGB images, you would end up with one tensor of 6 channels.
Your question does not make any sense if it can’t be interpreted in real life. For example in medical imaging, it is possible to use multiple images coming from ultrasound, MRI, CT, etc if they come from the same patient/view, there is a real meaning in doing this (contrast/resolution/etc).
To conclude unless the two images represent a sequence (video, audio), I don’t think it’s interesting to do this. Think of how you are going to predict something during inference.
If you want to do this anyway, I would suggest using torch.cat to concatenate a tensor along a given dimension.

http://pytorch.org/docs/torch.html#torch.cat 62