Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62397
Title: Detail-preserving and content-aware variational multi-view stereo reconstruction
Authors: Li, Z
Wang, K
Zuo, W
Meng, D
Zhang, L 
Keywords: Multi-view stereo
Reprojection error
Feature-preserving
l(p) minimization
Mesh denoising
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2016, v. 25, no. 2, p. 864-877 How to cite?
Journal: IEEE transactions on image processing 
Abstract: Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view images is a fundamental yet active research area in computer vision. Despite the steady progress in multi-view stereo (MVS) reconstruction, many existing methods are still limited in recovering fine-scale details and sharp features while suppressing noises, and may fail in reconstructing regions with less textures. To address these limitations, this paper presents a detail-preserving and content-aware variational (DCV) MVS method, which reconstructs the 3D surface by alternating between reprojection error minimization and mesh denoising. In reprojection error minimization, we propose a novel inter-image similarity measure, which is effective to preserve fine-scale details of the reconstructed surface and builds a connection between guided image filtering and image registration. In mesh denoising, we propose a content-aware l(p)-minimization algorithm by adaptively estimating the p value and regularization parameters. Compared with conventional isotropic mesh smoothing approaches, the proposed method is much more promising in suppressing noise while preserving sharp features. Experimental results on benchmark data sets demonstrate that our DCV method is capable of recovering more surface details, and obtains cleaner and more accurate reconstructions than the state-of-the-art methods. In particular, our method achieves the best results among all published methods on the Middlebury dino ring and dino sparse data sets in terms of both completeness and accuracy.
URI: http://hdl.handle.net/10397/62397
ISSN: 1057-7149
EISSN: 1941-0042
DOI: 10.1109/TIP.2015.2507400
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

1
Citations as of Dec 4, 2017

WEB OF SCIENCETM
Citations

1
Last Week
0
Last month
Citations as of Dec 8, 2017

Page view(s)

75
Last Week
1
Last month
Checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.