  Deep learning based 3D stereo networks give superior performance compared to
2D networks and conventional stereo methods. However, this improvement in the
performance comes at the cost of increased computational complexity, thus
making these networks non-practical for the real-world applications.
Specifically, these networks use 3D convolutions as a major work horse to
refine and regress disparities. In this work first, we show that these 3D
convolutions in stereo networks consume up to 94% of overall network operations
and act as a major bottleneck. Next, we propose a set of "plug-&-run" separable
convolutions to reduce the number of parameters and operations. When integrated
with the existing state of the art stereo networks, these convolutions lead up
to 7x reduction in number of operations and up to 3.5x reduction in parameters
without compromising their performance. In fact these convolutions lead to
improvement in their performance in the majority of cases.
