discrete geometry lecture 2 © alexander & michael bronstein tosca.cs.technion.ac.il/book...
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Discrete geometryLecture 2
© Alexander & Michael Bronsteintosca.cs.technion.ac.il/book
Numerical geometry of non-rigid shapesStanford University, Winter 2009
3Numerical geometry of non-rigid shapes Discrete geometry
Discretization
Surface
Metric
Topology
Sampling
Discrete metric (matrix of
distances)
Discrete topology
(connectivity)
Continuous world Discrete world
5Numerical geometry of non-rigid shapes Discrete geometry
Sampling density
How to quantify density of sampling?
is an -covering of if
Alternatively:
for all , where
is the point-to-set distance.
6Numerical geometry of non-rigid shapes Discrete geometry
Sampling efficiency
Also an r-covering!
Are all points necessary?
An -covering may be unnecessarily
dense (may even not be a discrete set).
Quantify how well the
samples are separated.
is -separated if
for all .
For , an -separated
set is finite if is compact.
7Numerical geometry of non-rigid shapes Discrete geometry
Farthest point sampling
Good sampling has to be dense and efficient at the same time.
Find and -separated -covering of .
Achieved using farthest point sampling.
We defer the discussion on
How to select ?
How to compute ?
9Numerical geometry of non-rigid shapes Discrete geometry
Farthest point sampling
Start with some .
Determine sampling radius
If stop.
Find the farthest point from
Add to
10Numerical geometry of non-rigid shapes Discrete geometry
Farthest point sampling
Outcome: -separated -covering of .
Produces sampling with progressively increasing density.
A greedy algorithm: previously added points remain in .
There might be another -separated -covering containing less
points.
In practice used to sub-sample a densely sampled shape.
Straightforward time complexity:
number of points in dense sampling, number of points in .
Using efficient data structures can be reduced to .
11Numerical geometry of non-rigid shapes Discrete geometry
Sampling as representation
Sampling represents a region on as a single point .
Region of points on closer to than to any other
Voronoi region (a.k.a. Dirichlet or Voronoi-Dirichlet region, Thiessen
polytope or polygon, Wigner-Seitz zone, domain of action).
To avoid degenerate cases, assume points in in general position:
No three points lie on the same geodesic.
(Euclidean case: no three collinear points).
No four points lie on the boundary of the same metric ball.
(Euclidean case: no four cocircular points).
12Numerical geometry of non-rigid shapes Discrete geometry
Voronoi decomposition
A point can belong to one of the following
Voronoi region ( is closer to than to any other ).
Voronoi edge ( is equidistant from and
).
Voronoi vertex ( is equidistant from
three points ).
Voronoi region Voronoi edge Voronoi vertex
14Numerical geometry of non-rigid shapes Discrete geometry
Voronoi decomposition
Voronoi regions are disjoint.
Their closure
covers the entire .
Cutting along Voronoi edges produces
a collection of tiles .
In the Euclidean case, the tiles are
convex polygons.
Hence, the tiles are topological disks
(are homeomorphic to a disk).
15Numerical geometry of non-rigid shapes Discrete geometry
Voronoi tesellation
Tessellation of (a.k.a. cell complex):
a finite collection of disjoint open topological
disks, whose closure cover the entire .
In the Euclidean case, Voronoi
decomposition is always a tessellation.
In the general case, Voronoi regions might
not be topological disks.
A valid tessellation is obtained is the
sampling is sufficiently dense.
16Numerical geometry of non-rigid shapes Discrete geometry
Non-Euclidean Voronoi tessellations
Convexity radius at a point is the largest for which the
closed ball is convex in , i.e., minimal geodesics between
every lie in .
Convexity radius of = infimum of convexity radii over all .
Theorem (Leibon & Letscher, 2000):
An -separated -covering of with convexity radius of
is guaranteed to produce a valid Voronoi tessellation.
Gives sufficient sampling density conditions.
17Numerical geometry of non-rigid shapes Discrete geometry
Sufficient sampling density conditions
Invalid tessellation Valid tessellation
19Numerical geometry of non-rigid shapes Discrete geometry
Farthest point samplingand Voronoi decomposition
MATLAB® intermezzo
20Numerical geometry of non-rigid shapes Discrete geometry
Representation error
Voronoi decomposition replaces with the closest point
.
Mapping copying each into .
Quantify the representation error introduced by .
Let be picked randomly with uniform distribution on .
Representation error = variance of
21Numerical geometry of non-rigid shapes Discrete geometry
Optimal sampling
In the Euclidean case:
(mean squared error).
Optimal sampling: given a fixed sampling size , minimize error
Alternatively: Given a fixed representation error , minimize sampling
size
22Numerical geometry of non-rigid shapes Discrete geometry
Centroidal Voronoi tessellation
In a sampling minimizing , each has to satisfy
(intrinsic centroid)
In the Euclidean case – center of mass
In general case: intrinsic centroid of .
Centroidal Voronoi tessellation (CVT): Voronoi tessellation
generated
by in which each is the intrinsic centroid of .
23Numerical geometry of non-rigid shapes Discrete geometry
Lloyd-Max algorithm
Start with some sampling (e.g., produced by FPS)
Construct Voronoi decomposition
For each , compute intrinsic centroids
Update
In the limit , approaches the hexagonal
honeycomb shape – the densest possible tessellation.
Lloyd-Max algorithms is known under many other names: vector
quantization, k-means, etc.
24Numerical geometry of non-rigid shapes Discrete geometry
Sampling as clustering
Partition the space into clusters with centers
to minimize some cost function
Maximum cluster radius
Average cluster radius
In the discrete setting, both problems are NP-hard
Lloyd-Max algorithm, a.k.a. k-means is a heuristic, sometimes minimizing
average cluster radius (if converges globally – not guaranteed)
25Numerical geometry of non-rigid shapes Discrete geometry
Farthest point sampling encore
Start with some ,
For
Find the farthest point
Compute the sampling radius
Lemma
.
28Numerical geometry of non-rigid shapes Discrete geometry
Almost optimal sampling
Theorem (Hochbaum & Shmoys, 1985)
Let be the result of the FPS algorithm. Then
In other words: FPS is worse than optimal sampling by at most 2.
29Numerical geometry of non-rigid shapes Discrete geometry
Idea of the proof
Let denote the optimal clusters, with centers
Distinguish between two cases
One of the clusters contains two
or more of the points
Each cluster contains exactly
one of the points
30Numerical geometry of non-rigid shapes Discrete geometry
Proof (first case)
Assume one of the clusters contains
two or more of the points ,
e.g.
triangle inequality
Hence:
31Numerical geometry of non-rigid shapes Discrete geometry
Proof (second case)
Assume each of the clusters
contains exactly one of the points
, e.g.
triangle inequality
Then, for any point