1 numerical geometry of non-rigid shapes fast marching methods fast marching methods lecture 3 ©...
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![Page 1: 1 Numerical geometry of non-rigid shapes Fast Marching Methods Fast marching methods Lecture 3 © Alexander & Michael Bronstein tosca.cs.technion.ac.il/book](https://reader038.vdocuments.us/reader038/viewer/2022103123/56649d3e5503460f94a17cba/html5/thumbnails/1.jpg)
1Numerical geometry of non-rigid shapes Fast Marching Methods
Fast marching methodsLecture 3
© Alexander & Michael Bronsteintosca.cs.technion.ac.il/book
Numerical geometry of non-rigid shapesStanford University, Winter 2009
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2Numerical geometry of non-rigid shapes Fast Marching Methods
Metric discretization
Discretized shape
Discrete metric
Metrication error
“Samplingtheorem”
Discretized metric
Approach I: discrete metric
Approach II: consistently discretized metric
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3Numerical geometry of non-rigid shapes Fast Marching Methods
Fast marching algorithms
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4Numerical geometry of non-rigid shapes Fast Marching Methods
Forest fire
Fire starts at a source at
.
Propagates with constant velocity
Arrives at time to a point
.
Fermat’s (least action) principle:
The fire chooses the quickest path
to travel.
Governs refraction laws in optics
(Snell’s law) and acoustics.
Fire arrival time =
distance map from source.
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5Numerical geometry of non-rigid shapes Fast Marching Methods
Distance maps on surfaces
Distance map on surface
Mapped locally to the tangent space
A small step in the direction changes the distance by
is directional derivative in the direction .
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6Numerical geometry of non-rigid shapes Fast Marching Methods
Intrinsic gradient
For some direction ,
The perpendicular direction is the direction of steepest
change of the distance map.
is referred to as the intrinsic gradient.
Formally, the intrinsic gradient of
function at a point
is a map
satisfying for any
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7Numerical geometry of non-rigid shapes Fast Marching Methods
Extrinsic gradient
Consider the distance map as a function .
The extrinsic gradient of at a point is a map
satisfying for any direction
In the standard Euclidean basis
Usually called “the gradient” of .
What is the connection between intrinsic and extrinsic gradients?
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8Numerical geometry of non-rigid shapes Fast Marching Methods
Intrinsic gradient = projection of
extrinsic gradient on tangent plane
In coordinates of a parametrization
,
is the Jacobian matrix
whose columns span .
Intrinsic and extrinsic gradients
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9Numerical geometry of non-rigid shapes Fast Marching Methods
Eikonal equation
Let be a minimal geodesic between and .
The derivative
is the fire front propagation direction.
In arclength parametrization .
Fermat’s principle:
Propagation direction = direction of steepest increase of .
Geodesic is perpendicular to the level sets of on .
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10Numerical geometry of non-rigid shapes Fast Marching Methods
Eikonal equation
Eikonal equation (from Greek εικων)
Hyperbolic PDE with boundary
condition
Minimal geodesics are
characteristics.
Describes propagation of waves
in medium.
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11Numerical geometry of non-rigid shapes Fast Marching Methods
Uniqueness of solution
In classic PDE theory, a solution
is a continuous differentiable
function satisfying
PDE theory guarantees existence
and uniqueness of solution.
Distance map is not everywhere
differentiable.
Solution is not unique!
1D example
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12Numerical geometry of non-rigid shapes Fast Marching Methods
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13Numerical geometry of non-rigid shapes Fast Marching Methods
Sub- and super-derivatives (1D case)
Superderivative: the set of all slopes above the graph
Subderivative: the set of all slopes below the graph
where is differentiable.
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14Numerical geometry of non-rigid shapes Fast Marching Methods
Viscosity solution
is a viscosity solution of the
1D eikonal equation if
Monotonicity: viscosity solution
does not have local maxima.
The largest among all
Existence and uniqueness
guaranteed.
Not a viscosity solution
Viscosity solution
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15Numerical geometry of non-rigid shapes Fast Marching Methods
Fast marching methods (FMM)
A family of numerical methods for
solving eikonal equation.
Finds the viscosity solution =
distance map.
Simulates wavefront propagation
from a source set.
A continuous variant of Dijkstra’s
algorithm.
Consistently approximate the
intrinsic metric on the surface.
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16Numerical geometry of non-rigid shapes Fast Marching Methods
Dijkstra’s algorithm
Initialize and for the rest of the graph;
Initialize queue of unprocessed vertices .
While
Find vertex with smallest value of ,
For each unprocessed adjacent vertex ,
Remove from .
Return distance map .
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17Numerical geometry of non-rigid shapes Fast Marching Methods
Fast marching algorithm
Initialize and mark it as black.
Initialize for other vertices and mark them as green.
Initialize queue of red vertices .
Repeat
Mark green neighbors of black vertices as red (add to )
For each red vertex
For each triangle sharing the vertex
Update from the triangle.
Mark with minimum value of as black (remove from )
Until there are no more green vertices.
Return distance map .
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18Numerical geometry of non-rigid shapes Fast Marching Methods
Update step
Dijkstra’s update
Vertex updated from
adjacent vertex
Distance computed
from
Path restricted to graph edges
Fast marching update
Vertex updated from
triangle
Distance computed
from and
Path can pass on mesh faces
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19Numerical geometry of non-rigid shapes Fast Marching Methods
Fast marching update step
Update from triangle
Compute from
and
Model wave front propagating from
planar source
unit propagation direction
source offset
Front hits at time
Hits at time
When does the front arrive to ?
Planar source
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20Numerical geometry of non-rigid shapes Fast Marching Methods
Fast marching update step
Assume w.l.o.g. and .
is given by the point-to-plane distance
Solve for parameters and using the point-to-plane distance
In vector notation
where , , and .
In a non-degenerate triangle matrix is full-rank
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21Numerical geometry of non-rigid shapes Fast Marching Methods
Fast marching update step
Apparently, we have two equations with three variables.
However, is a unit vector, hence .
where .
Substitute and obtain a quadratic equation
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22Numerical geometry of non-rigid shapes Fast Marching Methods
Causality condition
Quadratic equation is satisfied by both
and .
Two solutions for
Causality: front can propagate only
forward in time.
Causality condition
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23Numerical geometry of non-rigid shapes Fast Marching Methods
Causality condition
Causality condition
In other words
has to form obtuse angles with both
triangle edges .
Causality is required to obtain
consistent
approximation of the distance map.
Smallest solution for is inconsistent
and is discarded.
If largest solution is consistent, live the
largest solution!
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24Numerical geometry of non-rigid shapes Fast Marching Methods
Monotonicity condition
Viscosity solution has to be a monotonically increasing function.
Monotonicity condition: increase when or increase.
In other words:
Differentiate
w.r.t obtaining
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25Numerical geometry of non-rigid shapes Fast Marching Methods
Monotonicity condition
Substitute
Monotonicity satisfied when both coordinates of
have the same sign.
is positive definite
Causality condition:
Monotonicity condition:
At least one coordinate
of is negative
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26Numerical geometry of non-rigid shapes Fast Marching Methods
Since we have
Rows of are orthogonal to triangle edges
Monotonicity condition:
Geometric interpretation:
must form obtuse angles with normals
to triangle edges.
Said differently:
must come from within the triangle.
Monotonicity condition
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27Numerical geometry of non-rigid shapes Fast Marching Methods
Monotonicity condition: update direction
must come from within the triangle.
If it does not, project inside the triangle.
will coincide with one of the edges.
Update will reduce to Dijkstra’s update
One-sided update
or
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28Numerical geometry of non-rigid shapes Fast Marching Methods
Solve the quadratic equation and select the largest solution
Compute propagation direction
If monotonicity condition is violated,
Set
Fast marching update
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29Numerical geometry of non-rigid shapes Fast Marching Methods
Acute triangle
All directions in the triangle
satisfy causality and
monotonicity conditions.
Causality and monotonicity encore
Monotonicity
Causality
Causa
lity
Obtuse triangle
Some directions in the triangle
violate causality condition!
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30Numerical geometry of non-rigid shapes Fast Marching Methods
Fast marching on obtuse meshes
Inconsistent solution if the mesh contains obtuse triangles
Remeshing is costly
Solution: split obtuse triangles by adding virtual connections to
non-adjacent vertices
Done as a pre-processing step in
Kimmel & Sethian, “Computing geodesic paths on manifolds”, 1998
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31Numerical geometry of non-rigid shapes Fast Marching Methods
Mesh “unfolding”
Virtual connection splits obtuse angle into two acute ones
Kimmel & Sethian, “Computing geodesic paths on manifolds”, 1998
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32Numerical geometry of non-rigid shapes Fast Marching Methods
Fast marching
MATLAB® intermezzo
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33Numerical geometry of non-rigid shapes Fast Marching Methods
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34Numerical geometry of non-rigid shapes Fast Marching Methods
Parametrization of over .
Compute distance map ,
from source .
Chain rule
Extrinsic gradient in parametrization coordinates
Intrinsic gradient in parametrization coordinates
Eikonal equation on parametric surfaces
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35Numerical geometry of non-rigid shapes Fast Marching Methods
Eikonal equation on parametric surfaces
Eikonal equation in parametrization coordinates
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36Numerical geometry of non-rigid shapes Fast Marching Methods
Solve eikonal equation in parametrization domain
March on discretized parametrization domain.
We need to express update step in parametrization coordinates.
Fast marching on parametric surfaces
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37Numerical geometry of non-rigid shapes Fast Marching Methods
Cartesian sampling of with unit step.
Some connectivity (e.g. 4- or 8-neighbor).
Vertex updated from triangle
Assuming w.l.o.g.
or in matrix form
Fast marching on parametric surfaces
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38Numerical geometry of non-rigid shapes Fast Marching Methods
Inner product matrix
Describes triangle geometry.
lengths of the edges.
cosine of the angle.
Substitute into the update quadratic equation
Only first fundamental form coefficients and grid connectivity are
required for update.
Can measure distances when only surface gradients are known.
Fast marching on parametric surfaces
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39Numerical geometry of non-rigid shapes Fast Marching Methods
“Unfolding” on parametric surfaces
Virtual connections can be made directly in parametrizatio domain
Parametrization domain On the surface
Spira & Kimmel, “An efficient solution to the eikonal equation on parametric manifolds”, 2004
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40Numerical geometry of non-rigid shapes Fast Marching Methods
Heap-based grid update
Fast marching and Dijkstra’s algorithm use heap-based grid update.
Next vertex to be updated is decided by extracting the smallest .
Update order is unknown and data-dependent.
Inefficient use of memory system and cache.
Inherently sequential algorithm – next update depends on previous
one.
Can we do better?
Regular access to memory (known in advance).
Vectorizable (parallelizable) algorithm.
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41Numerical geometry of non-rigid shapes Fast Marching Methods
Marching even faster
Danielsson’s algorithm: update the grid in a raster scan order
In Euclidean case, parametrization is trivial.
Geodesics are straight lines in parametrization domain.
Each raster scan covers ¼ of the possible directions of the geodesics.
Euclidean distance map computed by four alternating raster scans.
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42Numerical geometry of non-rigid shapes Fast Marching Methods
Raster scan fast marching
Generally, geodesics are curved in
parametrization domain.
Raster scans have to be repeated to
produce a convergent solution.
Iterative algorithm.
Number of iterations depends on
geometry and parametrization.
Practically, few iterations are
required.
1 iteration
2 iterations
3 iterations
4 iterations
5 iterations
6 iterations
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43Numerical geometry of non-rigid shapes Fast Marching Methods
Raster scan fast marching
MATLAB® intermezzo
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44Numerical geometry of non-rigid shapes Fast Marching Methods
Raster scan fast marching
What we lost:
No more a one-pass algorithm.
Computational complexity is data-dependent.
What we found:
Coherent memory access, efficient use of cache.
No heap, each iteration is .
Raster scans can be parallelized.
BBK, "Parallel algorithms for approximation of distance maps on parametric surfaces”, 2007
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45Numerical geometry of non-rigid shapes Fast Marching Methods
Parallellization
Rotate scan directions by 450.
All updates performed along a row or column can be parallelized.
Constant CPU load – suitable for SIMD architecture and GPUs.
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46Numerical geometry of non-rigid shapes Fast Marching Methods
Parallel marching
Rotate scan directions by 450.
All updates performed along a row or column can be parallelized.
Constant CPU load.
Suitable for SIMD architecture and GPUs.
GPU implementation computes geodesic on grid with 10,000,000
vertices in less than 50 msec.
About 200 million distances per second!
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47Numerical geometry of non-rigid shapes Fast Marching Methods
Minimal geodesics
We have a numerical tool to compute geodesic distance.
Sometimes, the shortest path itself is needed.
Minimal geodesics are characteristics of the eikonal equation.
In other words:
Along geodesic, eikonal equation becomes an ODE
with initial condition .
Solve the ODE for .
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48Numerical geometry of non-rigid shapes Fast Marching Methods
Minimal geodesics
To find a minimal geodesic between two points
Compute distance map from to all other points.
Starting at , follow the direction of until is reached.
Steepest descent on the distance map.
In the parametrization coordinates
Let be the preimage of in
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49Numerical geometry of non-rigid shapes Fast Marching Methods
Minimal geodesics
Substitute into characteristic equation
Steepest descent on surface = scaled steepest descent in
parametrization domain.
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50Numerical geometry of non-rigid shapes Fast Marching Methods
Uses of fast marching
Geodesic distances
Minimal geodesics
Voronoi tessellation &
sampling
Offset curves
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51Numerical geometry of non-rigid shapes Fast Marching Methods
Shape represented as level set of some
Examples: medical images, shape-from-X reconstruction, etc.
Triangulation is costly and potentially inaccurate
Implicit surfaces
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52Numerical geometry of non-rigid shapes Fast Marching Methods
Implicit surfaces
Two-manifold
Co-dimension 1
Narrow band of radius
Three-manifold with
boundary
smooth if
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53Numerical geometry of non-rigid shapes Fast Marching Methods
Distances on implicit surfaces
Since , for all
Similarly, for ,
and hence
The sequence is bounded and nondecreasing and
hence converges to the supremum of its range
For every and there exists such
that
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54Numerical geometry of non-rigid shapes Fast Marching Methods
Distances on implicit surfaces
Uniform convergence of geodesic distances:
For every there exists an such that for every
,
If then
where is a constant dependent on the geometry of
Convergence of minimal geodesics:
Let be a unique minimal geodesic between
and let be a minimal
geodesic on . Then
Memoli & Sapiro, “Fast computation of weighted distance functions and geodesics on implicit hyper-surfaces”, 2001
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55Numerical geometry of non-rigid shapes Fast Marching Methods
Explicit
Intrinsic eikonal equation
Eikonal equation on implicit surfaces
Implicit
Extrinsic eikonal equation
VISCOSITY SOLUTIONS CONVERGE AS
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56Numerical geometry of non-rigid shapes Fast Marching Methods
Narrow band fast marching
Euclidean fast marching on Cartesian grid
Only vertices inside narrow band do not participate in update
Initial values of source set interpolated on the grid
Heap or raster scan grid visiting