Open AccessSingle image‐based 3D scene estimation from semantic prior
Author(s) -
Hwang Hyeong Jae,
Yoon Sang Min
Publication year - 2015
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2015.1458
Subject(s) - prior probability , artificial intelligence , computer science , computer vision , image (mathematics) , sequence (biology) , perception , 3d reconstruction , iterative reconstruction , semantics (computer science) , pattern recognition (psychology) , bayesian probability , genetics , neuroscience , biology , programming language
Reconstructing a three‐dimensional (3D) structure from a single image sequence to provide relevant contextual information for better human visual perception is a fundamental problem in computer vision. A 3D scene estimation methodology from a segmented image sequence that is learned from semantic priors is proposed. In particular, semantic information including 3D geometric characteristics can very efficiently predict the 3D structure of the scene from a given semantic region. The approach, which utilises semantic priors to estimate a 3D scene, is very robust for direct 3D scene reconstruction from an ambiguous depth map. The efficiency and effectiveness of the proposed approach has been proven experimentally with a large database.