Hi,
Here is the definition in the PWC-net

We can think of two signals you mentioned as two feature maps from CNN, let’s say fmap1 [48, 128, 64] and fmap2 [48, 128, 64] where [H, W, C]. Then “result in another signal” should be the cost volume.
The way to calculate the cost volume should be like this:
For each feature in fmap1 [i, j, 128], we find its corresponding neighbor in fmap2: [i-3, j-3, 128] [i-2,j-3, 128]…[i, j, 128]…[i+3, j+3, 128]. Then we do dot product between feature1 [i, j,  128] between each feature2 then we got N correlation values where N is the number of pixels located in neighbor, and we cat those scalars we get [1, 1, N] correlation feature. Finally loop over all pixels in fmap1 then get a cost volume as [48, 128, N] which stores the correlation value and represents similarity between fmap1 and fmap2