  In recent years, monocular depth estimation is applied to understand the
surrounding 3D environment and has made great progress. However, there is an
ill-posed problem on how to gain depth information directly from a single
image. With the rapid development of deep learning, this problem is possible to
be solved. Although more and more approaches are proposed one after another,
most of existing methods inevitably lost details due to continuous downsampling
when mapping from RGB space to depth space. To the end, we design a Multi-scale
Features Network (MSFNet), which consists of Enhanced Diverse Attention (EDA)
module and Upsample-Stage Fusion (USF) module. The EDA module employs the
spatial attention method to learn significant spatial information, while USF
module complements low-level detail information with high-level semantic
information from the perspective of multi-scale feature fusion to improve the
predicted effect. In addition, since the simple samples are always trained to a
better effect first, the hard samples are difficult to converge. Therefore, we
design a batch-loss to assign large loss factors to the harder samples in a
batch. Experiments on NYU-Depth V2 dataset and KITTI dataset demonstrate that
our proposed approach is more competitive with the state-of-the-art methods in
both qualitative and quantitative evaluation.
