Piecewise-Planar 3D Reconstruction with Edge and Corner Regularization

This paper presents a method for the 3D reconstruction of a piecewise‐planar surface from range images, typically laser scans with millions of points. The reconstructed surface is a watertight polygonal mesh that conforms to observations at a given scale in the visible planar parts of the scene, and...

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Veröffentlicht in:	Computer graphics forum Jg. 33; H. 5; S. 55 - 64
Hauptverfasser:	Boulch, Alexandre, de La Gorce, Martin, Marlet, Renaud
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Blackwell Publishing Ltd 01.08.2014 Wiley
Schlagworte:	3-D graphics Analysis Anisotropy Categories and Subject Descriptors (according to ACM CCS) Computational Geometry Computer Science Computer Vision and Pattern Recognition Corners I.2.10 [Artificial Intelligence]: Vision and Scene Understanding-3D/stereo scene analysis I.4.8 [Image Processing and Computer Vision]: Scene Analysis-Range data I.5.4 [Pattern Recognition]: Applications-Computer vision Linear programming Magnetorheological fluids Mathematical models Optimization Polygons Reconstruction Regularization Regularization methods Studies Three dimensional Topological manifolds
ISSN:	0167-7055, 1467-8659
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Abstract	This paper presents a method for the 3D reconstruction of a piecewise‐planar surface from range images, typically laser scans with millions of points. The reconstructed surface is a watertight polygonal mesh that conforms to observations at a given scale in the visible planar parts of the scene, and that is plausible in hidden parts. We formulate surface reconstruction as a discrete optimization problem based on detected and hypothesized planes. One of our major contributions, besides a treatment of data anisotropy and novel surface hypotheses, is a regularization of the reconstructed surface w.r.t. the length of edges and the number of corners. Compared to classical area‐based regularization, it better captures surface complexity and is therefore better suited for man‐made environments, such as buildings. To handle the underlying higher‐order potentials, that are problematic for MRF optimizers, we formulate minimization as a sparse mixed‐integer linear programming problem and obtain an approximate solution using a simple relaxation. Experiments show that it is fast and reaches near‐optimal solutions.
AbstractList	This paper presents a method for the 3D reconstruction of a piecewise-planar surface from range images, typically laser scans with millions of points. The reconstructed surface is a watertight polygonal mesh that conforms to observations at a given scale in the visible planar parts of the scene, and that is plausible in hidden parts. We formulate surface reconstruction as a discrete optimization problem based on detected and hypothesized planes. One of our major contributions, besides a treatment of data anisotropy and novel surface hypotheses, is a regularization of the reconstructed surface w.r.t. the length of edges and the number of corners. Compared to classical area-based regularization, it better captures surface complexity and is therefore better suited for man-made environments, such as buildings. To handle the underlying higher-order potentials, that are problematic for MRF optimizers, we formulate minimization as a sparse mixed-integer linear programming problem and obtain an approximate solution using a simple relaxation. Experiments show that it is fast and reaches near-optimal solutions. [PUBLICATION ABSTRACT] This paper presents a method for the 3D reconstruction of a piecewise‐planar surface from range images, typically laser scans with millions of points. The reconstructed surface is a watertight polygonal mesh that conforms to observations at a given scale in the visible planar parts of the scene, and that is plausible in hidden parts. We formulate surface reconstruction as a discrete optimization problem based on detected and hypothesized planes. One of our major contributions, besides a treatment of data anisotropy and novel surface hypotheses, is a regularization of the reconstructed surface w.r.t. the length of edges and the number of corners. Compared to classical area‐based regularization, it better captures surface complexity and is therefore better suited for man‐made environments, such as buildings. To handle the underlying higher‐order potentials, that are problematic for MRF optimizers, we formulate minimization as a sparse mixed‐integer linear programming problem and obtain an approximate solution using a simple relaxation. Experiments show that it is fast and reaches near‐optimal solutions. This paper presents a method for the 3D reconstruction of a piecewise-planar surface from range images, typi-cally laser scans with millions of points. The reconstructed surface is a watertight polygonal mesh that conforms to observations at a given scale in the visible planar parts of the scene, and that is plausible in hidden parts. We formulate surface reconstruction as a discrete optimization problem based on detected and hypothesized planes. One of our major contributions, besides a treatment of data anisotropy and novel surface hypotheses, is a regu-larization of the reconstructed surface w.r.t. the length of edges and the number of corners. Compared to classical area-based regularization, it better captures surface complexity and is therefore better suited for man-made en-vironments, such as buildings. To handle the underlying higher-order potentials, that are problematic for MRF optimizers, we formulate minimization as a sparse mixed-integer linear programming problem and obtain an ap-proximate solution using a simple relaxation. Experiments show that it is fast and reaches near-optimal solutions.
Author	Boulch, Alexandre de La Gorce, Martin Marlet, Renaud
Author_xml	– sequence: 1 givenname: Alexandre surname: Boulch fullname: Boulch, Alexandre organization: Université Paris-Est, LIGM (UMR CNRS 8049), ENPC, F-77455 Marne-la-Vallée – sequence: 2 givenname: Martin surname: de La Gorce fullname: de La Gorce, Martin organization: Université Paris-Est, LIGM (UMR CNRS 8049), ENPC, F-77455 Marne-la-Vallée – sequence: 3 givenname: Renaud surname: Marlet fullname: Marlet, Renaud organization: Université Paris-Est, LIGM (UMR CNRS 8049), ENPC, F-77455 Marne-la-Vallée
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Cites_doi	10.1109/CVPR.2010.5539824 10.1109/CVPR.2013.175 10.1109/CVPR.2009.5206867 10.1109/TVCG.2012.30 10.1109/IROS.2008.4650729 10.1109/ICPR.2014.171 10.1109/ICCV.2009.5459209 10.1111/j.1467-8659.2012.03181.x 10.1007/s00371-012-0749-1 10.1109/TPAMI.2004.1262177 10.1111/j.1467-8659.2009.01389.x 10.1111/j.1467-8659.2007.01016.x 10.1111/cgf.12042 10.1111/cgf.12170 10.1007/978-3-642-23094-3_15 10.1007/s11263-007-0105-5 10.1145/1964921.1964947 10.1111/cgf.12188
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Copyright	2014 The Author(s) Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2014 The Eurographics Association and John Wiley & Sons Ltd. Copyright
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References	Paulsen R.R., Baerentzen J.A., Larsen R.: Markov random field surface reconstruction. TVCG 16 (2010). 2 Fayolle P.-A., Pasko A.: Segmentation of discrete point clouds using an extensible set of templates. Vis. Comput. 29, 5 (2013), 449-465. 2 Shekhovtsov A., Kohli P., Rother C.: Curvature prior for MRF-based segmentation and shape inpainting. In DAGM/OAGM Symposium (2012), vol. 7476 of LNCS. 2 2013; 29 2010; 16 2012 2012; 7476 2011 2010 2009 2008 2007 2004 2014 2013 2002 e_1_2_7_5_2 e_1_2_7_4_2 e_1_2_7_3_2 e_1_2_7_2_2 e_1_2_7_9_2 e_1_2_7_8_2 e_1_2_7_7_2 e_1_2_7_6_2 e_1_2_7_18_2 e_1_2_7_17_2 e_1_2_7_16_2 Shekhovtsov A. (e_1_2_7_24_2) 2012; 7476 e_1_2_7_15_2 e_1_2_7_14_2 e_1_2_7_13_2 e_1_2_7_12_2 e_1_2_7_11_2 e_1_2_7_10_2 e_1_2_7_26_2 e_1_2_7_27_2 e_1_2_7_25_2 e_1_2_7_23_2 Paulsen R.R. (e_1_2_7_19_2) 2010; 16 e_1_2_7_22_2 e_1_2_7_21_2 e_1_2_7_20_2
References_xml	– reference: Fayolle P.-A., Pasko A.: Segmentation of discrete point clouds using an extensible set of templates. Vis. Comput. 29, 5 (2013), 449-465. 2 – reference: Paulsen R.R., Baerentzen J.A., Larsen R.: Markov random field surface reconstruction. TVCG 16 (2010). 2 – reference: Shekhovtsov A., Kohli P., Rother C.: Curvature prior for MRF-based segmentation and shape inpainting. In DAGM/OAGM Symposium (2012), vol. 7476 of LNCS. 2 – year: 2011 – year: 2009 – start-page: 205 year: 2011 end-page: 218 – volume: 16 year: 2010 article-title: Markov random field surface reconstruction publication-title: TVCG – volume: 29 start-page: 449 issue: 5 year: 2013 end-page: 465 article-title: Segmentation of discrete point clouds using an extensible set of templates publication-title: Vis. Comput – year: 2002 – year: 2008 – year: 2007 – year: 2004 – start-page: 217 year: 2013 end-page: 228 – volume: 7476 year: 2012 article-title: Curvature prior for MRF‐based segmentation and shape inpainting. In DAGM/OAGM Symposium publication-title: LNCS – start-page: 1261 year: 2010 end-page: 1268 – start-page: 3378 year: 2008 end-page: 3383 – start-page: 80 year: 2009 end-page: 87 – year: 2014 – year: 2010 – year: 2013 – year: 2012 – ident: e_1_2_7_10_2 doi: 10.1109/CVPR.2010.5539824 – ident: e_1_2_7_2_2 – ident: e_1_2_7_15_2 doi: 10.1109/CVPR.2013.175 – ident: e_1_2_7_8_2 – ident: e_1_2_7_11_2 doi: 10.1109/CVPR.2009.5206867 – ident: e_1_2_7_26_2 doi: 10.1109/TVCG.2012.30 – ident: e_1_2_7_20_2 doi: 10.1109/IROS.2008.4650729 – ident: e_1_2_7_6_2 doi: 10.1109/ICPR.2014.171 – volume: 16 year: 2010 ident: e_1_2_7_19_2 article-title: Markov random field surface reconstruction publication-title: TVCG – ident: e_1_2_7_23_2 doi: 10.1109/ICCV.2009.5459209 – ident: e_1_2_7_12_2 – ident: e_1_2_7_5_2 doi: 10.1111/j.1467-8659.2012.03181.x – ident: e_1_2_7_13_2 doi: 10.1007/s00371-012-0749-1 – ident: e_1_2_7_16_2 doi: 10.1109/TPAMI.2004.1262177 – volume: 7476 year: 2012 ident: e_1_2_7_24_2 article-title: Curvature prior for MRF‐based segmentation and shape inpainting. In DAGM/OAGM Symposium publication-title: LNCS – ident: e_1_2_7_21_2 doi: 10.1111/j.1467-8659.2009.01389.x – ident: e_1_2_7_14_2 – ident: e_1_2_7_3_2 – ident: e_1_2_7_25_2 doi: 10.1111/j.1467-8659.2007.01016.x – ident: e_1_2_7_17_2 doi: 10.1111/cgf.12042 – ident: e_1_2_7_4_2 doi: 10.1111/cgf.12170 – ident: e_1_2_7_22_2 doi: 10.1007/978-3-642-23094-3_15 – ident: e_1_2_7_9_2 doi: 10.1007/s11263-007-0105-5 – ident: e_1_2_7_18_2 doi: 10.1145/1964921.1964947 – ident: e_1_2_7_7_2 – ident: e_1_2_7_27_2 doi: 10.1111/cgf.12188
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Snippet	This paper presents a method for the 3D reconstruction of a piecewise‐planar surface from range images, typically laser scans with millions of points. The... This paper presents a method for the 3D reconstruction of a piecewise-planar surface from range images, typically laser scans with millions of points. The... This paper presents a method for the 3D reconstruction of a piecewise-planar surface from range images, typi-cally laser scans with millions of points. The...
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SubjectTerms	3-D graphics Analysis Anisotropy Categories and Subject Descriptors (according to ACM CCS) Computational Geometry Computer Science Computer Vision and Pattern Recognition Corners I.2.10 [Artificial Intelligence]: Vision and Scene Understanding-3D/stereo scene analysis I.4.8 [Image Processing and Computer Vision]: Scene Analysis-Range data I.5.4 [Pattern Recognition]: Applications-Computer vision Linear programming Magnetorheological fluids Mathematical models Optimization Polygons Reconstruction Regularization Regularization methods Studies Three dimensional Topological manifolds
Title	Piecewise-Planar 3D Reconstruction with Edge and Corner Regularization
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