3d convolution calculator

1. What is a 3D convolution?
2. How does a 3D CNN work?
3. What is the difference between CNN and 3D CNN?
4. What is the difference between 2D and 3D convolution?

What is a 3D convolution?

A 3D Convolution is a type of convolution where the kernel slides in 3 dimensions as opposed to 2 dimensions with 2D convolutions. One example use case is medical imaging where a model is constructed using 3D image slices.

How does a 3D CNN work?

3D convolutions applies a 3 dimentional filter to the dataset and the filter moves 3-direction (x, y, z) to calcuate the low level feature representations. Their output shape is a 3 dimentional volume space such as cube or cuboid. They are helpful in event detection in videos, 3D medical images etc.

What is the difference between CNN and 3D CNN?

In 2D CNN, kernel moves in 2 directions. Input and output data of 2D CNN is 3 dimensional. Mostly used on Image data. In 3D CNN, kernel moves in 3 directions.

What is the difference between 2D and 3D convolution?

(a) 2D convolutions use the same weights for the whole depth of the stack of frames (multiple channels) and results in a single image. (b) 3D convolutions use 3D filters and produce a 3D volume as a result of the convolution, thus preserving temporal information of the frame stack.