  Depth estimation from a single image is a challenging problem in computer
vision because binocular disparity or motion information is absent. Whereas
impressive performances have been reported in this area recently using
end-to-end trained deep neural architectures, as to what cues in the images
that are being exploited by these black box systems is hard to know. To this
end, in this work, we quantify the relative contributions of the known cues of
depth in a monocular depth estimation setting using an indoor scene data set.
Our work uses feature extraction techniques to relate the single features of
shape, texture, colour and saturation, taken in isolation, to predict depth. We
find that the shape of objects extracted by edge detection substantially
contributes more than others in the indoor setting considered, while the other
features also have contributions in varying degrees. These insights will help
optimise depth estimation models, boosting their accuracy and robustness. They
promise to broaden the practical applications of vision-based depth estimation.
The project code is attached to the supplementary material and will be
published on GitHub.
