We compare five monocular depth estimation methods based on deep learning. This
comparison focuses on how well methods generalize rather than a quantitative comparison
on a specific dataset. This study shows that while monocular depth estimation methods work
well on images similar to training images, they often show artifacts when applied on images
out of the training distribution. We evaluate the different methods with images similar to
training data and images with unusual point of views (eg top-down) or paintings. The …