Estimating relative depth in single images via rankboost

In this paper, we present a novel approach to estimate the relative depth of regions in monocular images. There are several contributions. First, the task of monocular depth estimation is considered as a learning-to-rank problem which offers several advantages compared to regression approaches. Second, monocular depth clues of human perception are modeled in a systematic manner. Third, we show that these depth clues can be modeled and integrated appropriately in a Rankboost framework. For this purpose, a space-efficient version of Rankboost is derived that makes it applicable to rank a large number of objects, as posed by the given problem. Finally, the monocular depth clues are combined with results from a deep learning approach. Experimental results show that the error rate is reduced by adding the monocular features while outperforming state-of-the-art systems.