Monocular Depth Estimation Using Whole Strip Masking and Reliability-Based Refinement

Minhyeok Heo, Jaehan Lee, Kyung-Rae Kim, Han-Ul Kim, Chang-Su Kim; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 36-51

We propose a monocular depth estimation algorithm, which extracts a depth map from a single image, 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 the 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. Extensive experimental results demonstrate that the proposed algorithm provides the state-of-the-art depth estimation performance, outperforming conventional algorithms significantly.