Authors: Yunfeng Hu, Matthew Hudelson, Bala Krishnamoorthy, Altansuren Tumurbaatar and Kevin Vixie.
Full paper can be found at https://jocg.org/index.php/jocg/article/view/3078.
Introduction
The authors suggest the textbook Geometric Measure Theory: A Beginner’s Guide for an introduction to geometric measure theory.
Shapes as Currents
A -current in is any element of the dual space of smooth, compactly supported -forms in (so a linear functional mapping -forms to ). Intuitively, one may view them as a union of a finite number of pieces of oriented -dimensional smooth manifolds in with an orienting -vector field on the submanifolds.
A Hausdorff measure of a set is defined using efficient covers of . Intuitively, is the -dimensional volume of ; we compute it as
where the ‘s are the collections of sets such that and , and is the volume of the unit ball in .
Remark: In , , i.e., the Hausdorff measure is the same as the Lebesgue measure.
Rectifiable set
A set is a -rectifiable subset of if
where each of the are Lipschitz, and the -Hausdorff measure .
Rectifiable Currents
We say is a rectifiable current if there is a -vector field in , an integer valued function , and a rectifiable set with such that for any -form ,
Based on figure 3, it seems a rectifiable current is obtained by orienting a rectifiable set. The boundary of a -current is then the -current given by
where denotes the exterior derivative of the -form .
The Multiscale Flat Norm
Given a -current , we decompose into two components: where is any -current. Then the flat norm of is given by the current that results in the smallest mass, i.e.,
where is the space of -currents and
is the mass of a current .
The multiscale flat norm is a simple generalization of the flat norm given by
Means and Medians in the Space of Integral Currents
Given a set of integral -currents , we define their mean as
and their median as
where is the set of all integral -currents. We are also interested in the mass regularized version of the mean and median:
The median can be computed efficiently by a linear optimization problem.
Median Shapes on Simplicial Complexes: Preliminaries
We know represent integral -currents as -chains in a simplicial complex of dimension . More specifically, the case in which is a finite simplicial complex.
Let , , denote the -simplices and , , denote the -simplices of . To find the simplicial flat norm of the integral current represented by the -chain
with underlying group , we look for candidate -chains
which defines the decomposition as
Note then that and are homologous -chains. The flat norm decomposition is then given by the pair of chains which minimizes the sum of weighted volumes of these chains, i.e.,
where is the -dimensional volume of simplex . The volume is equivalent to the mass of the -simplex . Therefore, we may define the multiscale flat norm for a simplicial complex -chain as
Recall that we can capture the boundary operator by the -boundary matrix of where when and otherwise. More specifically, indicates that and they have the same orientation.
Simplicial Median Shape and Integer Linear Optimization
The authors view the median shape problem in terms of simplices and present and IP for the simplicial median shape problem. Since the homology groups are over , we insist on integral solutions. Even though the IP is not unimodular, the LP relaxation of the IP has always provided integral solutions in practice.
Median Shape as an Integer Program
Let denote the group of -chains of the simplicial complex . We view a set of currents as -chains . Then the simplicial median shape is the -chain in which
is minimized, i.e.,
Denote by and a potential flat norm decomposition of , i.e., . Denote by the -th component of . Denote the volumes by and . Then the median shape problem can be viewed as the following optimization problem:
Note that is given by the matrix product , where is the -boundary matrix of , and hence linear. However, and is not linear. We fix this by splitting each the parameters and into two terms each such that
Under this transformation, we now get an IP:
See the paper (page 54) for the IP in matrix form.
Total Unimodularity and the Median Shape LP
We relax the IP to an LP by ignoring the integrality constraints.
Totally Unimodular
A totally unimodular matrix is an integral matrix for which every square submatrix has determinant , , or .
TU is preserved under the following operations.
- Permuting rows or columns.
- Taking the transpose.
- Multiplying a row or column by .
- Adding a row or column of all zeros, or adding a row or column with one nonzero that is .
- Repeating a row or column.
Def
The -fold -sum of an matrix is the matrix
Remark: The -fold -sum is denoted by a in a circle. There does not appear to be a way to write this in without tikz. In my notes I will use a box.
While the -sum preserve TU, the -fold -sum does not.
Generalizations of the Median Shape LP
We can modify the median shape LP to find a median shape with mass regularization, by replacing with and adding to the objective function. We require that , and typically .
One could also take the generalized weighted simplicial median shape problem which seeks that minimizes
Complexity of Simplicial Median Shape
We wish to analyze the computational complexity of the mass-regularized weighted simplicial median shape problem (MRWSMSP). We consider the decision version of the MRWSMSP to get the DMRWSMSP.
Consider input -chains , -chain , and pairs of and -chains such that . Then given weights and parameters we define the function
Then the DMRWSMSP can be viewed as whether there is an input in which where is some given rational number.
Lemma
DMRWSMSP is NP-complete and MRWSMSP is NP-hard.
A special case of DMRWSMSP is the optimal homologous chain problem (OHCP) which seeks to find a chain with the minimal total weight in the same homology class as the input chain in a finite simplicial complex. That is, DMRWSMSP with a single input chain gives the OHCP.
Computational Experiments
In the figure below is a simple mesh with 3851 edges and 2510 triangles. There are two input 1-chains (green and red). The authors chose and . The mass-regularized median shape is shown in black.
Full code: https://github.com/tbtraltaa/medianshape