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Spectral surface reconstruction. Reporter: Lincong Fang 24th Sep, 2008. Point clouds. Surface reconstruction. Unorganized Unoriented (no oriented normals) Non-uniform, sparse sampling Possibly with noise. Applications. Computer Graphics Medical Imaging Computer-aided Design - PowerPoint PPT Presentation
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Spectral surface reconstruction
Reporter: Lincong Fang24th Sep, 2008
Point clouds
Surface reconstruction
Unorganized Unoriented (no oriented normals) Non-uniform, sparse sampling Possibly with noise
Applications
Computer Graphics Medical Imaging Computer-aided Design Solid Modeling
Approaches
Delaunay\Voronoi based Implicit surfaces Deformable models Spectral Etc.
Approaches
Delaunay\Voronoi based
Unorganized, unoriented, non-uniform, noise
Approaches
Implicit surfaces
Unorganized, unoriented, non-uniform, noise
Approaches
Deformable models
Adrei Sharf, Thomas Lewiner, Ariel Shamir, Leif Kobbelt, Daniel Cohen–OR. Competing fronts for coarse–to–fine surface reconstruction. EG2006
Approaches
Delaunay\Voronoi based Implicit surfaces Deformable models Spectral Etc.[1] R. Kolluri, J. Richard Shewchuk, J. F. O’Brien, Spectral surface reconstruction from noisy point clouds. SGP 2004.[2] P. Alliez, D. Cohen-Steiner, Y. Tong, M. Desbrun Voronoi-based variational reconstruction of unoriented point sets. SGP 2007.
Spectral surface reconstruction from noisy point clouds R. Kolluri (Google) J. Richard Shewchuk J. F. O’Brien University of Califonia, Berkeley
SGP 2004
The eigencrust algorithm
Partition the tetrahedra of a Delaunay tetrahedralization into inside/outside
Identify the triangular faces that interface between the subgraphs
Poles
Nina Amenta, Marshall Bern, Manolis Kamvysselis. A new Voronoi-based surface reconstruction algorithm. SigGraph 98
Pole graph G
( , )G V E
:1.( , ), , are poles of
2.( , '),( , '), ( ', ), ( , '),
, are poles of , ', '
are poles of ', and ( , ')
is an edge of the Delaunay
tetrahedralization
E u v u v s
u u u v u v v v
u v s u v
s s s
Pole graph GThe negatively weighted edges of the pole graph
Pole graph GThe positively weighted edges of pole graph
Weights
4 4cos,u v e 4 4cos
,u v e
Super node->G’
: outside polesO super node z
, ,z v u O u v
Pole matrix
,i jij ji v vL L | |ii ijj iL L
Lx Dx ii iiD L
ix one node of G' one pole one tetrahedron
: the eigenvector associated with the
smallest eigenvalue
x
Remaining tetrahedra
Graph H: two supernodes (inside and outside)
Edge: if two unlabeled tetrahedra share a triangular face
Edge to one of the supernodes: if an unlabeled tetrahedron
shares a face with labeled tetrahedron
max
min
Weight(aspect ratio):e
e
One negative edge weight:
an edge connecting the
inside and outside supernodes
The final mesh
The final mesh is the “eigencrust” The triangles where the inside and
outside tetrahedra meet
Results
If all adjacent tetrahedra are labeled the same, the point is an outlier
Undersampled regions are handled without holes
More results
Efficacy
2008414 input pointsTetrahedralize:13.5 minutes
157 minutes
265minutes
Voronoi-based variational reconstruction of unoriented point sets P. Alliez D. Cohen-Steiner Y. Tong M. Desbrun
SGP 2007 (best paper award)
Pierre Alliez Researcher at INRIA in the GEOMETRICA
group Research
Geometry Processing: geometry compression, surface approximation, mesh parameterization, surface remeshing and mesh generation
Avid user of the CGAL library CGAL developer
David Cohen-Steiner Researcher at INRIA in the GEOMETRICA
team Research
Approximation problems in applied geometry and topology
Meshes and point clouds are of particular interest
Yiying Tong Assistant Professor Computer Science and Engineering
Dept. at Michigan State University Research
Computer simulation/animation Discrete geometric modeling Discrete differential geometry Face recognition using 3D models
Mathieu Desbrun Associate Professor in Computer
Science and Computational Science & Engineering
California Institute of Technology Research
Applying discrete differential geometry to a wide range of fields and applications
Overview
Point setPoint setTensor Tensor
estimationestimationImplicit functionImplicit function
+ contouring+ contouring
Tensor estimation
Normal estimation(PCA)
1 1
T
i i
ik ik
p p p p
C
p p p p
l l lC v v
0( ) : ( ) 0T x x p v
Voronoi PCA
Noise-free case
Noise-free vs noisy
Noisy case
Implicit function
TensorsTensors Implicit functionImplicit function
Delaunay refinement
Delaunay refinement
Variational formulation
Find implicit function f such that its gradient f best aligns to the principal component of the tensors
Anisotropic Dirichlet energyMeasures alignment with tensors
Biharmonic energyMeasures smoothness of ff
Regularization
Rationale
Anisotropic tensors: favor alignment
Isotropic tensors: favor smoothness
Rationale
Anisotropic tensors: favor alignment Isotropic tensors: favor smoothness
Large aligned gradients + smoothness
->consistent orientation of f
Solver
A: Anisotropic Laplacian operator
B: Isotropic Bilaplacian operator
Desbrun M, Kanso E, Tong Y. Discrete differential forms forComputational modeling. In Discrete Differential Geometry.ACM SIGGRAPH Course, 2006.
V vertices {vi}E edges {ei}
Tensor C F=(f1,f2,…,fv)t
Solver
10 0*t
cA d d 10 0*tB d d
0: vertex/edge incident matrix( )td E V
1 1(* ) (* ) , 1...ti i
c ii iiti i
e Cei E
e e
*1 | |
(* )| |
iii
i
e
e
Generalized eigenvalue problem
(1 )t tE F AF F BF
AF BF
/ 0E F
maxEigenvector(PWL function)
Standard eigenvalueproblem
Compute Cholesky factorization of B(TAUCS)
tB LL
11
tt t t t
t
L AL G GAF LL F L AL L F L F
G L F
Solver: Implicitly restarted Arnoldi method (ARPACK++)
Contouring
F=(f1,f2,…,fv)t
Sparse sampling
Noise
Nested components
Comparison
PoissonPoisson GEPGEP
Poisson reconstruction
Comparison
Poisson reconstruction
Sforz(250K points)
Out-of-core factorization25 minutes
Conclusion
Pros Handles unoriented point sets Handles noisy point sets
Cons Slow Not easy to implement