University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Segmentation based features for wide-baseline multi-view reconstruction

Mustafa, A, Kim, H, Imre, H and Hilton, A (2015) Segmentation based features for wide-baseline multi-view reconstruction In: International Conference on 3D Vision (3DV), 19-22 October 2015, Lyon, France.

3DV_CameraReady.pdf - ["content_typename_Accepted version (post-print)" not defined]
Available under License : See the attached licence file.

Download (3MB) | Preview
Text (licence)
Available under License : See the attached licence file.

Download (33kB) | Preview


A common problem in wide-baseline stereo is the sparse and non-uniform distribution of correspondences when using conventional detectors such as SIFT, SURF, FAST and MSER. In this paper we introduce a novel segmentation based feature detector SFD that produces an increased number of ‘good’ features for accurate wide-baseline reconstruction. Each image is segmented into regions by over-segmentation and feature points are detected at the intersection of the boundaries for three or more regions. Segmentation-based feature detection locates features at local maxima giving a relatively large number of feature points which are consistently detected across wide-baseline views and accurately localised. A comprehensive comparative performance evaluation with previous feature detection approaches demonstrates that: SFD produces a large number of features with increased scene coverage; detected features are consistent across wide-baseline views for images of a variety of indoor and outdoor scenes; and the number of wide-baseline matches is increased by an order of magnitude compared to alternative detector-descriptor combinations. Sparse scene reconstruction from multiple wide-baseline stereo views using the SFD feature detector demonstrates at least a factor six increase in the number of reconstructed points with reduced error distribution compared to SIFT when evaluated against ground-truth and similar computational cost to SURF/FAST.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Mustafa, A
Kim, H
Imre, H
Hilton, A
Date : 19 October 2015
DOI : 10.1109/3DV.2015.39
Additional Information : © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 03 Nov 2015 09:59
Last Modified : 31 Oct 2017 17:48

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800