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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Vydáno v:Computer graphics forum Ročník 33; číslo 5; s. 55 - 64
Hlavní autoři: Boulch, Alexandre, de La Gorce, Martin, Marlet, Renaud
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Blackwell Publishing Ltd 01.08.2014
Wiley
Témata:
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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Popis
Shrnutí: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.
Bibliografie:istex:06A479C05F3BCD262C4BD0DA344814633A163223
ArticleID:CGF12431
ark:/67375/WNG-21GHDZQH-B
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12431