@inproceedings{44a70e2dd16347d8b540648bd2c2caac,
title = "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.",
keywords = "Depth map refinement, Monocular depth estimation, Reliability, Whole strip masking",
author = "Minhyeok Heo and Jaehan Lee and Kim, \{Kyung Rae\} and Kim, \{Han Ul\} and Kim, \{Chang Su\}",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 15th European Conference on Computer Vision, ECCV 2018 ; Conference date: 08-09-2018 Through 14-09-2018",
year = "2018",
doi = "10.1007/978-3-030-01225-0\_3",
language = "English",
isbn = "9783030012243",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "39--55",
editor = "Vittorio Ferrari and Cristian Sminchisescu and Yair Weiss and Martial Hebert",
booktitle = "Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings",
}