Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/636277
Title: Detail-Preserving and Content-Aware Variational Multi-View Stereo Reconstruction
Authors: Zhaoxin Li;Kuanquan Wang;Wangmeng Zuo;Deyu Meng;Lei Zhang
subject: reprojection error|mesh denoising|Multi-view stereo|featurepreserving|`p minimization
Year: 2016
Publisher: IEEE
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 &#x2113;<sub>p</sub>-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://localhost/handle/Hannan/168118
http://localhost/handle/Hannan/636277
ISSN: 1057-7149
1941-0042
volume: 25
issue: 2
Appears in Collections:2016

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Title: Detail-Preserving and Content-Aware Variational Multi-View Stereo Reconstruction
Authors: Zhaoxin Li;Kuanquan Wang;Wangmeng Zuo;Deyu Meng;Lei Zhang
subject: reprojection error|mesh denoising|Multi-view stereo|featurepreserving|`p minimization
Year: 2016
Publisher: IEEE
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 &#x2113;<sub>p</sub>-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://localhost/handle/Hannan/168118
http://localhost/handle/Hannan/636277
ISSN: 1057-7149
1941-0042
volume: 25
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7350114.pdf10.92 MBAdobe PDFThumbnail
Preview File
Title: Detail-Preserving and Content-Aware Variational Multi-View Stereo Reconstruction
Authors: Zhaoxin Li;Kuanquan Wang;Wangmeng Zuo;Deyu Meng;Lei Zhang
subject: reprojection error|mesh denoising|Multi-view stereo|featurepreserving|`p minimization
Year: 2016
Publisher: IEEE
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 &#x2113;<sub>p</sub>-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://localhost/handle/Hannan/168118
http://localhost/handle/Hannan/636277
ISSN: 1057-7149
1941-0042
volume: 25
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7350114.pdf10.92 MBAdobe PDFThumbnail
Preview File