Currently, I’m working using 3D convolution and multiple input images. Compared to 2D convolution, 3D convolution considers the relationship among the adjacent images (also video frames) along with the time domain, which means the depth dimension. Five input images can be regarded as five video frames. For that reason, we need to expand the depth or channel dimensions to extract the features among the images in the time domain as [batch * channel(# of channels of each image) * depth(# of frames) * height * width].