  Dense optical flow estimation is challenging when there are large
displacements in a scene with heterogeneous motion dynamics, occlusion, and
scene homogeneity. Traditional approaches to handle these challenges include
hierarchical and multiresolution processing methods. Learning-based optical
flow methods typically use a multiresolution approach with image warping when a
broad range of flow velocities and heterogeneous motion is present. Accuracy of
such coarse-to-fine methods is affected by the ghosting artifacts when images
are warped across multiple resolutions and by the vanishing problem in smaller
scene extents with higher motion contrast. Previously, we devised strategies
for building compact dense prediction networks guided by the effective
receptive field (ERF) characteristics of the network (DDCNet). The DDCNet
design was intentionally simple and compact allowing it to be used as a
building block for designing more complex yet compact networks. In this work,
we extend the DDCNet strategies to handle heterogeneous motion dynamics by
cascading DDCNet based sub-nets with decreasing extents of their ERF. Our
DDCNet with multiresolution capability (DDCNet-Multires) is compact without any
specialized network layers. We evaluate the performance of the DDCNet-Multires
network using standard optical flow benchmark datasets. Our experiments
demonstrate that DDCNet-Multires improves over the DDCNet-B0 and -B1 and
provides optical flow estimates with accuracy comparable to similar lightweight
learning-based methods.
