Monocular Depth Estimation Using Relative Depth Maps

被引：92

作者：

Lee, Jae-Han ^{[1
]}

Kim, Chang-Su ^{[1
]}

机构：

[1] Korea Univ, Seoul, South Korea

来源：

2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) | 2019年

基金：

新加坡国家研究基金会;

关键词：

D O I：

10.1109/CVPR.2019.00996

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose a novel algorithm for monocular depth estimation using relative depth maps. First, using a convolutional neural network, we estimate relative depths between pairs of regions, as well as ordinary depths, at various scales. Second, we restore relative depth maps from selectively estimated data based on the rank-1 property of pairwise comparison matrices. Third, we decompose ordinary and relative depth maps into components and recombine them optimally to reconstruct a final depth map. Experimental results show that the proposed algorithm provides the state-of-art depth estimation performance.

引用

页码：9721 / 9730

页数：10