Monocular Depth Estimation Using Whole Strip
Masking and Reliability-Based Refinement
Abstract. We propose a monocular depth estimation algorithm based
on whole strip masking (WSM) and reliability-based refinement. First,
we develop a convolutional neural network (CNN) tailored for the depth
estimation. Specifically, we design a novel filter, called WSM, to exploit
the tendency that a scene has similar depths in horizonal or vertical
directions. The proposed CNN combines WSM upsampling blocks with
a ResNet encoder. Second, we measure the reliability of an estimated
depth, by appending additional layers to the main CNN. Using the reliability information, we perform conditional random field (CRF) optimization to refine the estimated depth map. Experimental results demonstrate that the proposed algorithm provides the state-of-the-art depth
estimation performance