The University of Southampton
University of Southampton Institutional Repository

Block world reconstruction from spherical stereo image pairs

Block world reconstruction from spherical stereo image pairs
Block world reconstruction from spherical stereo image pairs
We propose a block-based scene reconstruction method using multiple stereo pairs of spherical images. We assume that the urban scene consists of axis-aligned planar structures (Manhattan world). Captured spherical stereo images are converted into six central-point perspective images by cubic projection and façade alignment. Depth information is recovered by stereo matching between images. Semantic regions are segmented based on colour, edge and normal information. Independent 3D rectangular planes are constructed by fitting planes aligned with the principal axes of the segmented 3D points. Finally cuboid-based scene structure is recovered from multiple viewpoints by merging and refining planes based on connectivity and visibility. The reconstructed model efficiently shows the structure of the scene with a small amount of data. © 2015 Elsevier Inc. All rights reserved.
Image processing, Image reconstruction, Semantics, Spheres, Three dimensional computer graphics, 3D reconstruction, Block world interpretation, Depth information, Multiple viewpoints, Perspective image, Planar structure, Scene reconstruction, Spherical imaging, Stereo image processing
104-121
Kim, H.
2c7c135c-f00b-4409-acb2-85b3a9e8225f
Hilton, Adrian
12782a55-4c4d-4dfb-a690-62505f6665db
Kim, H.
2c7c135c-f00b-4409-acb2-85b3a9e8225f
Hilton, Adrian
12782a55-4c4d-4dfb-a690-62505f6665db

Kim, H. and Hilton, Adrian (2015) Block world reconstruction from spherical stereo image pairs. Computer Vision and Image Understanding, 139, 104-121. (doi:10.1016/j.cviu.2015.04.001).

Record type: Article

Abstract

We propose a block-based scene reconstruction method using multiple stereo pairs of spherical images. We assume that the urban scene consists of axis-aligned planar structures (Manhattan world). Captured spherical stereo images are converted into six central-point perspective images by cubic projection and façade alignment. Depth information is recovered by stereo matching between images. Semantic regions are segmented based on colour, edge and normal information. Independent 3D rectangular planes are constructed by fitting planes aligned with the principal axes of the segmented 3D points. Finally cuboid-based scene structure is recovered from multiple viewpoints by merging and refining planes based on connectivity and visibility. The reconstructed model efficiently shows the structure of the scene with a small amount of data. © 2015 Elsevier Inc. All rights reserved.

Full text not available from this repository.

More information

Accepted/In Press date: 6 April 2015
e-pub ahead of print date: 21 August 2015
Keywords: Image processing, Image reconstruction, Semantics, Spheres, Three dimensional computer graphics, 3D reconstruction, Block world interpretation, Depth information, Multiple viewpoints, Perspective image, Planar structure, Scene reconstruction, Spherical imaging, Stereo image processing

Identifiers

Local EPrints ID: 440585
URI: http://eprints.soton.ac.uk/id/eprint/440585
PURE UUID: 021f673f-00a8-4d85-9128-5f253d9067e4
ORCID for H. Kim: ORCID iD orcid.org/0000-0003-4907-0491

Catalogue record

Date deposited: 07 May 2020 16:38
Last modified: 23 May 2020 00:47

Export record

Altmetrics

Contributors

Author: H. Kim ORCID iD
Author: Adrian Hilton

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×