Guide to Computational Geometry Processing: Foundations, by Jakob Andreas Bærentzen, Jens Gravesen, François Anton,

By Jakob Andreas Bærentzen, Jens Gravesen, François Anton, Henrik Aanæs

This ebook experiences the algorithms for processing geometric information, with a realistic concentrate on very important thoughts no longer coated by means of conventional classes on laptop imaginative and prescient and special effects. positive factors: offers an outline of the underlying mathematical concept, overlaying vector areas, metric house, affine areas, differential geometry, and finite distinction tools for derivatives and differential equations; reports geometry representations, together with polygonal meshes, splines, and subdivision surfaces; examines ideas for computing curvature from polygonal meshes; describes algorithms for mesh smoothing, mesh parametrization, and mesh optimization and simplification; discusses element position databases and convex hulls of aspect units; investigates the reconstruction of triangle meshes from aspect clouds, together with equipment for registration of element clouds and floor reconstruction; offers extra fabric at a supplementary site; contains self-study workouts in the course of the text.

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Optical scanning is quickly changing into ubiquitous. From business laser scanners to clinical CT, MR and 3D ultrasound scanners, a variety of agencies now have quick access to optical acquisition units that offer large volumes of snapshot information. even if, the uncooked geometry facts bought needs to first be processed sooner than it really is precious. This consultant to Computational Geometry Processing reports the algorithms for processing geometric facts, with a pragmatic concentrate on very important suggestions no longer coated through conventional classes on desktop imaginative and prescient and special effects. this is often balanced with an creation to the theoretical and mathematical underpinnings of every procedure, permitting the reader not to in basic terms enforce a given approach, but additionally to appreciate the information in the back of it, its obstacles and its merits. subject matters and contours: provides an summary of the underlying mathematical conception, protecting vector areas, metric house, affine areas, differential geometry, and finite distinction equipment for derivatives and differential equationsReviews geometry representations, together with polygonal meshes, splines, and subdivision surfacesExamines thoughts for computing curvature from polygonal meshesDescribes algorithms for mesh smoothing, mesh parametrization, and mesh optimization and simplificationDiscusses aspect situation databases and convex hulls of element setsInvestigates the reconstruction of triangle meshes from aspect clouds, together with equipment for registration of aspect clouds and floor reconstructionProvides extra fabric at a supplementary websiteIncludes self-study routines in the course of the textGraduate scholars will locate this article a beneficial, hands-on advisor to constructing key talents in geometry processing. The ebook also will function an invaluable reference for pros wishing to enhance their competency during this quarter.

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Example text

6. If K = 0 and not both principal curvatures are zero then we have a parabolic point, and finally if both principal curvatures are zero then we have a planar point. On a negatively curved surface there are two directions where the normal curvature is zero and they are called the asymptotic directions. The mean curvature is the mean value of the principal curvatures, κ1 + κ2 − tr W tr(I−1I ) = = 2 2 2 1 g11 b22 − 2g12 b12 + g22 b11 . 7 The Gaußian and Mean Curvature 53 Fig. 6 On the left a positively curved surface, on the right a negatively curved surface Fig.

If this gives f1 , . . , fk then the rest of the basis vectors are chosen as an orthonormal basis for span{f1 , . . , fk }⊥ . In terms of matrices it has the following formulation. 12 Let A be an m × n matrix and let k = min{m, n}. Then A can be decomposed as A = UΣVT , where U is an orthogonal m × m matrix, V is an orthogonal n × n matrix, and Σ is a diagonal matrix with non zero elements σ1 ≥ σ1 ≥ · · · ≥ σk ≥ 0 in the diagonal. The singular values are the square root of the eigenvalues of AT A, which is a positive semi definite symmetric matrix.

6 Let h be the signed distance function for a surface S and let e1 , e2 , e3 be pairwise orthogonal eigenvectors for the Hessian H (h) such that e3 = ∇h. The principal directions for S are then e1 and e2 and if λ1 , λ2 , λ3 are the eigenvalues for H (h), then λ3 = 0 and the principal curvatures are κ1 = −λ1 and κ2 = −λ2 . We find in particular that the mean curvature is M= λ1 + λ2 tr H (h) 1 ∂ 2h ∂ 2h ∂ 2h κ1 + κ2 =− =− =− + + 2 , 2 2 2 2 ∂x 2 ∂y 2 ∂z 60 3 Differential Geometry and the Gaußian curvature is K = κ1 κ2 = λ1 λ2 = ∂2h ∂x 2 ∂2h ∂y∂x ∂2h ∂x∂y ∂2h ∂y 2 + ∂2h ∂x 2 ∂ 2h ∂z∂x ∂2h ∂x∂z ∂2h ∂z2 + ∂2h ∂y 2 ∂2h ∂y∂z ∂2h ∂y∂z ∂2h ∂z2 .

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