  This paper presents a novel neural architecture search method, called LiDNAS,
for generating lightweight monocular depth estimation models. Unlike previous
neural architecture search (NAS) approaches, where finding optimized networks
are computationally highly demanding, the introduced novel Assisted Tabu Search
leads to efficient architecture exploration. Moreover, we construct the search
space on a pre-defined backbone network to balance layer diversity and search
space size. The LiDNAS method outperforms the state-of-the-art NAS approach,
proposed for disparity and depth estimation, in terms of search efficiency and
output model performance. The LiDNAS optimized models achieve results superior
to compact depth estimation state-of-the-art on NYU-Depth-v2, KITTI, and
ScanNet, while being 7%-500% more compact in size, i.e the number of model
parameters.
