  Dense pixel matching problems such as optical flow and disparity estimation
are among the most challenging tasks in computer vision. Recently, several deep
learning methods designed for these problems have been successful. A
sufficiently larger effective receptive field (ERF) and a higher resolution of
spatial features within a network are essential for providing higher-resolution
dense estimates. In this work, we present a systemic approach to design network
architectures that can provide a larger receptive field while maintaining a
higher spatial feature resolution. To achieve a larger ERF, we utilized dilated
convolutional layers. By aggressively increasing dilation rates in the deeper
layers, we were able to achieve a sufficiently larger ERF with a significantly
fewer number of trainable parameters. We used optical flow estimation problem
as the primary benchmark to illustrate our network design strategy. The
benchmark results (Sintel, KITTI, and Middlebury) indicate that our compact
networks can achieve comparable performance in the class of lightweight
networks.
