  Accurate depth maps are essential in various applications, such as autonomous
driving, scene reconstruction, point-cloud creation, etc. However,
monocular-depth estimation (MDE) algorithms often fail to provide enough
texture & sharpness, and also are inconsistent for homogeneous scenes. These
algorithms mostly use CNN or vision transformer-based architectures requiring
large datasets for supervised training. But, MDE algorithms trained on
available depth datasets do not generalize well and hence fail to perform
accurately in diverse real-world scenes. Moreover, the ground-truth depth maps
are either lower resolution or sparse leading to relatively inconsistent depth
maps. In general, acquiring a high-resolution ground truth dataset with
pixel-level precision for accurate depth prediction is an expensive, and
time-consuming challenge.
  In this paper, we generate a high-resolution synthetic depth dataset (HRSD)
of dimension 1920 X 1080 from Grand Theft Auto (GTA-V), which contains 100,000
color images and corresponding dense ground truth depth maps. The generated
datasets are diverse and have scenes from indoors to outdoors, from homogeneous
surfaces to textures. For experiments and analysis, we train the DPT algorithm,
a state-of-the-art transformer-based MDE algorithm on the proposed synthetic
dataset, which significantly increases the accuracy of depth maps on different
scenes by 9 %. Since the synthetic datasets are of higher resolution, we
propose adding a feature extraction module in the transformer encoder and
incorporating an attention-based loss, further improving the accuracy by 15 %.
