  We introduce Repetition-Reduction network (RRNet) for resource-constrained
depth estimation, offering significantly improved efficiency in terms of
computation, memory and energy consumption. The proposed method is based on
repetition-reduction (RR) blocks. The RR blocks consist of the set of repeated
convolutions and the residual connection layer that take place of the pointwise
reduction layer with linear connection to the decoder. The RRNet help reduce
memory usage and power consumption in the residual connections to the decoder
layers. RRNet consumes approximately 3.84 times less energy and 3.06 times less
meory and is approaximately 2.21 times faster, without increasing the demand on
hardware resource relative to the baseline network (Godard et al, CVPR'17),
outperforming current state-of-the-art lightweight architectures such as
SqueezeNet, ShuffleNet, MobileNet and PyDNet.
