  Standard 3D convolution operations require much larger amounts of memory and
computation cost than 2D convolution operations. The fact has hindered the
development of deep neural nets in many 3D vision tasks. In this paper, we
investigate the possibility of applying depthwise separable convolutions in 3D
scenario and introduce the use of 3D depthwise convolution. A 3D depthwise
convolution splits a single standard 3D convolution into two separate steps,
which would drastically reduce the number of parameters in 3D convolutions with
more than one order of magnitude. We experiment with 3D depthwise convolution
on popular CNN architectures and also compare it with a similar structure
called pseudo-3D convolution. The results demonstrate that, with 3D depthwise
convolutions, 3D vision tasks like classification and reconstruction can be
carried out with more light-weighted neural networks while still delivering
comparable performances.
