Indoor scene structure analysis for single image depth estimation

Date

2015

Authors

Zhuo, Wei
Salzmann, Mathieu
He, Xuming
Liu, Miaomiao

Journal Title

Journal ISSN

Volume Title

Publisher

IEEE

Abstract

We tackle the problem of single image depth estimation, which, without additional knowledge, suffers from many ambiguities. Unlike previous approaches that only reason locally, we propose to exploit the global structure of the scene to estimate its depth. To this end, we introduce a hierarchical representation of the scene, which models local depth jointly with mid-level and global scene structures. We formulate single image depth estimation as inference in a graphical model whose edges let us encode the interactions within and across the different layers of our hierarchy. Our method therefore still produces detailed depth estimates, but also leverages higher-level information about the scene. We demonstrate the benefits of our approach over local depth estimation methods on standard indoor datasets

Description

Keywords

Citation

Source

Exemplar Hidden Markov Models for Classification of Facial Expressions in Videos

Type

Conference paper

Book Title

Entity type

Access Statement

License Rights

DOI

10.1109/CVPR.2015.7298660

Restricted until

2037-12-31