Deep convolutional neural fields for depth estimation from a single image

We consider the problem of depth estimation from a sin-gle molecular image in this work. It is a challenging task as no reliable depth cues are available, e.g., stereo correspon-dences, motions etc. Previous efforts have been focusing on exploiting geometric priors or additional sources of in-formation, with all using hand-crafted features. Recently, there is mounting evidence that features from deep convo-lutional neural networks (CNN) are setting new records for various vision applications. On the other hand, considering the continuous characteristic of the depth values, depth esti-mations can be naturally formulated into a continuous con-ditional random field (CRF) learning problem. Therefore, we in this paper present a deep convolutional neural field model for estimating depths from a single image, aiming t