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

Depth Insight -- Contribution of Different Features to Indoor Single-image Depth Estimation