Recovering Relative Depth from Low-Level Features Without Explicit T-junction Detection and Interpretation

This work presents a novel computational model for relative depth order estimation from a single image based on low-level local features that encode perceptual depth cues such as convexity/concavity, inclusion, and T-junctions in a quantitative manner, considering information at different scales. These multi-scale features are based on a measure of how likely is a pixel to belong simultaneously to different objects (interpreted as connected components of level sets) and, hence, to be occluded in some of them, providing a hint on the local depth order relationships. They are directly computed on the discrete image data in an efficient manner, without requiring the detection and interpretation of edges or junctions. Its behavior is clarified and illustrated for some simple images. Then the recovery of the relative depth order on the image is achieved by global integration of these local features applying a non-linear diffusion filtering of bilateral type. The validity of the proposed features and the integration approach is demonstrated by experiments on real images and comparison with state-of-the-art monocular depth estimation techniques.