I see. My bad that I didn’t understand it.
There is still a disadvantage with Conv2d in cases like below:
Let’s say we have 6 frames with RGB channels.
With Conv3d, we can emulate applying a conv kernel for every 3 frames to learn short-range temporal features. i.e., with in_channels=3 &  kernel_size (3,5,5) for example. In this way, there is a possibility to learn motion features in a hierarchical way.
With Conv2d, I am not sure if we can emulate it. As you said, we can apply 18 x 5 x 5 kernel. But it may not be efficient as learning hierarchically.