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MultiView Stereo for Community Photo Collections
"... We present a multiview stereo algorithm that addresses the extreme changes in lighting, scale, clutter, and other effects in large online community photo collections. Our idea is to intelligently choose images to match, both at a perview and perpixel level. We show that such adaptive view selecti ..."
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Cited by 132 (17 self)
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We present a multiview stereo algorithm that addresses the extreme changes in lighting, scale, clutter, and other effects in large online community photo collections. Our idea is to intelligently choose images to match, both at a perview and perpixel level. We show that such adaptive view selection enables robust performance even with dramatic appearance variability. The stereo matching technique takes as input sparse 3D points reconstructed from structurefrommotion methods and iteratively grows surfaces from these points. Optimizing for surface normals within a photoconsistency measure significantly improves the matching results. While the focus of our approach is to estimate highquality depth maps, we also show examples of merging the resulting depth maps into compelling scene reconstructions. We demonstrate our algorithm on standard multiview stereo datasets and on casually acquired photo collections of famous scenes gathered from the Internet. 1
Least squares 3D surface and curve matching
 ISPRS Journal of Photogrammetry and Remote Sensing
, 2005
"... The automatic coregistration of point clouds, representing 3D surfaces, is a relevant problem in 3D modeling. This multiple registration problem can be defined as a surface matching task. We treat it as least squares matching of overlapping surfaces. The surface may have been digitized/sampled poin ..."
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Cited by 60 (13 self)
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The automatic coregistration of point clouds, representing 3D surfaces, is a relevant problem in 3D modeling. This multiple registration problem can be defined as a surface matching task. We treat it as least squares matching of overlapping surfaces. The surface may have been digitized/sampled point by point using a laser scanner device, a photogrammetric method or other surface measurement techniques. Our proposed method estimates the transformation parameters of one or more 3D search surfaces with respect to a 3D template surface, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formulation gives the opportunity of matching arbitrarily oriented 3D surface patches. It fully considers 3D geometry. Besides the mathematical model and execution aspects we address the further extensions of the basic model. We also show how this method can be used for curve matching in 3D space and matching of curves to surfaces. Some practical examples based on the registration of closerange laser scanner and photogrammetric point clouds are presented for the demonstration of the method. This surface matching technique is a generalization of the least squares image matching concept and offers high flexibility for any kind of 3D surface correspondence problem, as well as statistical tools for the analysis of the quality of final matching results.
Least squares 3D surface matching
 IAPRS, 34(5/W16), (on CDROM
, 2004
"... The automatic coregistration of point clouds, representing 3D surfaces, is a relevant problem in 3D modeling. This registration problem can be defined as a surface matching problem. We treat it as least squares matching of overlapping surfaces. The point cloud may have been digitized/sampled point ..."
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Cited by 20 (4 self)
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The automatic coregistration of point clouds, representing 3D surfaces, is a relevant problem in 3D modeling. This registration problem can be defined as a surface matching problem. We treat it as least squares matching of overlapping surfaces. The point cloud may have been digitized/sampled point by point using a laser scanner device, a photogrammetric method or other surface measurement techniques. In the past, several efforts have been made concerning the registration of 3D point clouds. One of the most popular methods is the Iterative Closest Point (ICP) algorithm. Several variations and improvements of the ICP method have been proposed. In photogrammetry there have been some studies on the absolute orientation of stereo models using DEMs (Digital Elevation Model) as control information. These works are known as DEM matching, which corresponds mathematically with least squares image matching. The DEM matching concept is only applied to 2.5D surfaces. 2.5D surfaces have limited value, especially in close range applications. Our proposed method estimates the 3D similarity transformation parameters between two or more fully 3D surface patches, minimizing the Euclidean distances between the surfaces by least squares. This formulation gives the opportunity of matching arbitrarily oriented 3D surface patches. An observation equation is written for each surface element on the template surface patch, i.e. for each sampled point. The geometric relationship between the conjugate surface patches is defined as a 7parameter 3D similarity transformation. The constant term of the adjustment is given by the observation vector whose elements are the Euclidean distances between the template and search surface elements. Since the functional model is nonlinear, the solution is iteratively approaching to a global minimum. The unknown transformation parameters are treated as stochastic quantities using
Registration of point clouds using range and intensity information
 PATERAKI AND M. BALTSAVIAS (EDS), INTERNATIONAL WORKSHOP ON RECORDING, MODELING AND VISUALIZATION OF CULTURAL HERITAGE
, 2005
"... An algorithm for the least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formul ..."
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Cited by 5 (0 self)
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An algorithm for the least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formulation gives the opportunity of matching arbitrarily oriented 3D surfaces simultaneously, without using explicit tie points. Besides the mathematical model and execution aspects we give further extensions of the basic model: simultaneous matching of multi subsurface patches, and matching of surface geometry and its attribute information, e.g. reflectance, color, temperature, etc. under a combined estimation model. We give practical examples for the demonstration of the basic method and the extensions.
A flexible mathematical model for matching of 3D surfaces and attributes
 Videometrics VIII, Proc. of SPIEIS&T Electronic Imaging
"... An algorithm for the least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formul ..."
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Cited by 2 (2 self)
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An algorithm for the least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formulation gives the opportunity of matching arbitrarily oriented 3D surfaces simultaneously, without using explicit tie points. Besides the mathematical model and execution aspects we give further extension of the basic model. The first extension is the simultaneous matching of subsurface patches, which are selected in cooperative surface areas. It provides a computationally effective solution, since it matches only relevant multisubpatches rather than the whole overlapping areas. The second extension is the matching of surface geometry and its attribute information, e.g. reflectance, color, temperature, etc., under a combined estimation model. We give practical examples for the demonstration of the basic method and the extensions.
A NEW ALGORITHM FOR 3D SURFACE MATCHING
"... A new algorithm for least squares matching of overlapping 3D surfaces, digitized/sampled point by point using a laser scanner device, the photogrammetric method or other techniques, is proposed. In photogrammetry, the problem statement of surface patch matching and its solution method was first addr ..."
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Cited by 1 (0 self)
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A new algorithm for least squares matching of overlapping 3D surfaces, digitized/sampled point by point using a laser scanner device, the photogrammetric method or other techniques, is proposed. In photogrammetry, the problem statement of surface patch matching and its solution method was first addressed by Gruen (1985a) as a straight application of Least Squares Matching. There have been some studies on the absolute orientation of stereo models using DEMs as control information. These works have been known as DEM matching. Furthermore, techniques for 2.5D DEM surface matching have been developed, which correspond mathematically with least squares image matching. 2.5D surfaces have limited value, especially in close range applications. Our proposed method estimates the transformation parameters between two or more fully 3D surface patches, minimizing the Euclidean distances instead of Zdifferences between the surfaces by least squares. This formulation gives the opportunity of matching arbitrarily oriented surface patches. An observation equation is written for each surface element on the template surface patch, i.e. for each sampled point. The geometric relationship between the conjugate surface patches is defined as a 7parameter 3D similarity transformation. The Least Squares observations of the adjustment are defined by the observation vector whose elements are Euclidean distances between the template and search surface elements. The unknown transformation parameters are treated as stochastic quantities using proper weights. This extension of the functional model gives control over the estimation parameters. The details of the mathematical modelling of the proposed method, the convergence behavior, and statistical analysis of the theoretical precision of the estimated parameters are explained. Furthermore, some experimental results based on registration of closerange
Coregistration of Surfaces by 3D Least Squares Matching
"... A method for the automatic coregistration of 3D surfaces is presented. The method utilizes the mathematical model of Least Squares 2D image matching and extends it for solving the 3D surface matching problem. The transformation parameters of the search surfaces are estimated with respect to a templ ..."
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Cited by 1 (0 self)
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A method for the automatic coregistration of 3D surfaces is presented. The method utilizes the mathematical model of Least Squares 2D image matching and extends it for solving the 3D surface matching problem. The transformation parameters of the search surfaces are estimated with respect to a template surface. The solution is achieved when the sum of the squares of the 3D spatial (Euclidean) distances between the surfaces are minimized. The parameter estimation is achieved using the Generalized GaussMarkov model. Execution level implementation details are given. Apart from the coregistration of the point clouds generated from spaceborne, airborne and terrestrial sensors and techniques, the proposed method is also useful for change detection, 3D comparison, and quality assessment tasks. Experiments using terrain data examples show the capabilities of the method.
Coregistration of pointclouds by 3D Least Squares matching
"... www.photogrammetry.ethz.ch The automatic coregistration of point clouds, representing 3D surfaces, is a relevant problem in 3D modeling. We treat the problem as least squares matching of overlapping surfaces. The surface may have been digitized/sampled point by point using a laser scanner device, a ..."
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www.photogrammetry.ethz.ch The automatic coregistration of point clouds, representing 3D surfaces, is a relevant problem in 3D modeling. We treat the problem as least squares matching of overlapping surfaces. The surface may have been digitized/sampled point by point using a laser scanner device, a photogrammetric method or other surface measurement techniques. Our proposed method estimates the transformation parameters of one or more 3D search surfaces with respect to a 3D template surface, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formulation gives the opportunity of matching arbitrarily oriented 3D surfaces. It fully considers 3D geometry. The method derives its mathematical strength from the Least Squares matching concept and offers a high level of flexibility for many kinds of 3D surface correspondence problems. The experiments demonstrate the capabilities of the basic method and the extensions. Examples on the terrain/object modeling, cultural heritage applications, accuracy assessment and change detection are presented. 1.
SEMIAUTOMATIC ORIENTATION OF IMAGES WITH RESPECT TO A POINT CLOUD SYSTEM
"... This paper presents the results of a master thesis in which it was tried to orient a set of images of an object to a point cloud of the same object. As test object the “Semper Sternwarte ” in Zürich was used. As data sets a dense point cloud from a laser scanner and a sparse point cloud obtained by ..."
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This paper presents the results of a master thesis in which it was tried to orient a set of images of an object to a point cloud of the same object. As test object the “Semper Sternwarte ” in Zürich was used. As data sets a dense point cloud from a laser scanner and a sparse point cloud obtained by photogrammetric means, as well as the orientation of the images were used. Precisely orientated images with respect to a point cloud can be used to incorporate an edgeconstrained triangulation techniques, blunder and outlier detection, which leads to an overall better representation of a 3D model. This paper looks at a specific semiautomated work flow using selfprogrammed tools and tries to determine whether the work flow is suitable for this task or not. 1.
Application Of Digital Photogrammetry In Geotechnics
, 2004
"... Acquiring geometric data, such as coordinates, displacements or deformations, has always been a way to verify mathematical modelling in civil engineering. The introduction of finite elements methods further reinforced this trend. This paper reports on two applications of photogrammetry to soil and r ..."
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Acquiring geometric data, such as coordinates, displacements or deformations, has always been a way to verify mathematical modelling in civil engineering. The introduction of finite elements methods further reinforced this trend. This paper reports on two applications of photogrammetry to soil and rock mechanics in order to provide an accurate and dense description of the deformation fields of sand specimens in different loading conditions. In the first case, the displacements induced by a foundation under load in sand layers are traced until the collapse of the terrain with accuracies in the order of about 20 micrometers in object space. In the latter, the trajectories of particles, tied to a sloping sand specimen that slides along a plane, are determined with an accuracy of about 3 mm at a rate of 22 fps. In both examples, the measurement runs automatically, the only interaction required being the test setup.