What is the need for so many filters in a CNN?

Consider the following coding line related to CNNS

Conv2D(64, (3,3), strides=(2, 2), padding='same')


It is a convolution layer with filter size $$3 \times 3$$ and step size of $$2\times 2$$.

I am confused about the need for $$64$$ filters.

Are they doing the same task? Obviously, it is no. (one is enough in this case)

Then how do each filter differ by? Is it in hovering over the input matrix? Or is it in the values contained by filter itself? Or differs in both hovering and content?

I am finding difficulty in visualizing it.