  Depth estimation from 2D images is a common computer vision task that has
applications in many fields including autonomous vehicles, scene understanding
and robotics. The accuracy of a supervised depth estimation method mainly
relies on the chosen loss function, the model architecture, quality of data and
performance metrics. In this study, we propose a simplified and adaptable
approach to improve depth estimation accuracy using transfer learning and an
optimized loss function. The optimized loss function is a combination of
weighted losses to which enhance robustness and generalization: Mean Absolute
Error (MAE), Edge Loss and Structural Similarity Index (SSIM). We use a grid
search and a random search method to find optimized weights for the losses,
which leads to an improved model. We explore multiple encoder-decoder-based
models including DenseNet121, DenseNet169, DenseNet201, and EfficientNet for
the supervised depth estimation model on NYU Depth Dataset v2. We observe that
the EfficientNet model, pre-trained on ImageNet for classification when used as
an encoder, with a simple upsampling decoder, gives the best results in terms
of RSME, REL and log10: 0.386, 0.113 and 0.049, respectively. We also perform a
qualitative analysis which illustrates that our model produces depth maps that
closely resemble ground truth, even in cases where the ground truth is flawed.
The results indicate significant improvements in accuracy and robustness, with
EfficientNet being the most successful architecture.
