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Some Useful Properties of the Permutohedral Lattice for Gaussian Filtering

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Some Useful Properties of the Permutohedral Lattice for Gaussian Filtering Some Useful Properties of the Permutohedral Lattice for Gaussian Filtering Jongmin Baek and Andrew Adams Stanford University The aim of this self-contained supplement is to rigorously Definition 1.1. A lattice L is a discrete additive subgroup of establish the mathematical properties of the permutohedral a Euclidean space. lattice, to adapt it for the use in Gaussian filtering, and to An additive subgroup of a Euclidean space is closed under further justify its use. For discussion of a CPU implementa- addition and subtraction, and contains the origin. Hence, one tion, applications and empirical comparison to other meth- can alternatively characterize a lattice as all linear combina- ods, see [ABD10]. tions of a set of vectors called the frame (akin to the basis The rest of the paper is organized as follows: in Section 1, of a vector space) with integer weights. For example, the lat- d we give preliminary definitions of relevant terms and no- tice Z corresponds to the frame consisting of standard basis tation; in Section 2, we formally define the permutohedral vectors ~u0,~u1,.... lattice in several ways and prove useful structural proper- The fact that a lattice is an additive group immediately ties stemming from the definition; in Section 3, we suggest gives rise to many useful properties, including translational criteria for picking the ideal lattice with which to perform symmetry; the local structure of a lattice around each lattice Gaussian filtering, and present an argument that the permu- point is invariant. A typical way to study the local structure tohedral lattice is the most appropriate choice within a large of any discrete point set is to examine its Voronoi cells: family of lattices; in Section 4, we discuss how to perform Rd each step of the Gaussian filter using the permutohedral lat- Definition 1.2. Let~x be a point in a lattice L . Then the Rd ⊂ tice. Finally, in Section 5, we analyze the overall algorithm Voronoi cell of ~x is the set of points in nearer to~x than to and compare it to the implementation on Zd in [PD09]. any other lattice point, i.e. ~y Rd ~x′ L, ~x ~y 2 ~x′ ~y 2 . ∈ ∀ ∈ k − k ≤ k − k 1. Preliminaries n o It is clear that the Voronoi cells of a lattice are uniform and We begin by declaring the notation used hereafter in Table 1. related to one another by translation. They also tessellate the Rd Rd Other standard notation such as R,Z applies. underlying space , since any point in can be associated with a lattice point nearest to it. d The dimensionality of the underlying space. Definition 1.3. A Delaunay cell of a lattice is the convex ~x A vector of dimensionality d +1, unless spec- hull of all lattice points whose Voronoi cells share a common ified otherwise: x0,x1,...,x . vertex. { d} ~1 The d + 1-dimensional vector 1,1,...,1 . { } The Delaunay cells are the dual of the Voronoi cells, and Hd The d-dimensional hyperplane yield a natural notion of “nearby” lattice points for an arbi- ~x ~x ~1 = 0 Rd+1. trary point in space. They also tessellate the space, but they { | · }⊂ need not be uniform. Figure 1 demonstrates the Voronoi and ∗ Ad The permutohedral lattice of dimensionality Delaunay cells for a pair of two-dimensional lattices. d, lying inside H . d Note that Rd in the above definitions can be replaced with T( ) The projection mapping Rd+1 onto H along d any set isometric to Rd . For instance, H is a d-dimensional · the vector~1. (See Proposition 2.3.) d hyperplane in Rd+1 and is therefore isometric to Rd. Any N(~x;θ) Multi-variate Gaussian of covariance matrix discrete additive subgroup of H is a lattice, and its Voronoi θI, evaluated at ~x. d and Delaunay cells resides in H . (This is the case with the ~u The k-th standard basis vector, indexed from d k permutohedral lattice, as we shall see shortly in Section 2.) zero, i.e. ~u0 = 1,0,0,...,0 . { } Table 1: Common notation used in the paper. 2. The Permutohedral Lattice The permutohedral lattice has seen applications in many Now we may review the basic definitions that will be use- fields ranging from crystallography to communication. It ful in the subsequent sections. Our first definition is, not sur- arises ubiquitously in nature; its two-dimensional and three- prisingly, the standard definition of a lattice: dimensional instances are the hexagonal honeycomb—see 2 J. Baek, & A. Adams / Some Useful Properties of the Permutohedral Lattice for Gaussian Filtering d A∗ := ~x H ~x is a remainder-k point. , (2.3) d { ∈ d | } k[=0 where we call ~x H a remainder-k point for some k ∈ d ∈ 0,...,d iff all coordinates are congruent to k modulo { } d + 1. Proof. (2.2) (2.3): Let~x (d + 1)Zd+1. Then we can write ⊆ ∈ ~x = (d + 1)~y for some ~y Zd+1. This yields, ∈ (d + 1)(~y ~1) T(~x) = (d + 1)~y · ~1 − d + 1 = (d + 1)~y (~y ~1)~1. − · We see that each component of T(~x) is an integer and has a Figure 1: Voronoi and Delaunay tessellations of some two- consistent remainder modulo d + 1, namely (~y ~1). Z2 ∗ · dimensional lattices. Top: the Voronoi cells of and A2 are shown, along with lattice points. The two lattices have (2.3) (2.1): Let ~x be a remainder-k point for some k ⊆ ∈ equal density. Bottom: the Delaunay cells induced by the 0,...,d . Then we can write ~x = (d + 1)~y + k~1 for some { } Voronoi cells above. All cells of Z2 are squares, whereas A∗ ~y Zd+1. To show that ~x/(d + 1) is in the dual of A , note 2 ∈ d has hexagonal Voronoi cells and triangular Delaunay cells. that for all~z A , ∈ d ~x k ~z = ~y + ~1 ~z d + 1 · d + 1 · Figure 1—and the face-centered cubic lattice seen in the k =~y ~z + ~z ~1 packing structure of diamond, respectively. We will define · d + 1 · the permutohedral lattice in several ways and derive its struc- =~y ~z Z, since~z ~1 = 0. tural properties. · ∈ · (2.1) (2.2): Let ~x/(d + 1) be in the dual lattice of A . Note ⊆ d that for all i = j, the vector ~ui ~u j belongs to A , because 2.1. Definition 6 − d (~ui ~u j) ~1 = 0. Hence, Traditionally, the permutohedral lattice is defined as the dual − · ~x xi x j of the root lattice Ad. Note that the dual of a lattice is not the (~ui ~u j) = Z. vertices of its Voronoi cells, as typically intuited; see Defini- d + 1 · − d + 1 − d + 1 ∈ tion 2.2. Both Ad and the permutohedral lattice are typically This implies that all pairs of coordinates xi,x j differ by mul- d+1 embedded in H R for ease of manipulation. tiples of d +1. Writing xi = (d +1)yi +k for some yi Z,k d ∈ ∈ ⊂ R, we see that~x is the projection of (d +1)~y obtained by sub- Definition 2.1. The root lattice A is d tracting a multiple of (1,...,1). Because~x H , this projec- ∈ d d+1 tion must be equivalent to T. ~x = (x0,...,x ) Z ~x ~1 = 0 . d ∈ · According to Proposition 2.3, the simplest way to charac- d+1 ∗ Zd+1 In other words, Ad = Z Hd . Quite clearly, Ad Hd. terize Ad is to project along the long diagonal vector ∩ ⊂ ~ Z ∗ 1, with a scalar factor of d + 1 to keep the coordinates in , Definition 2.2. The permutohedral lattice, denoted by Ad , is as seen in Figure 2. From now on, we will assume the scaled the dual lattice of Ad inside Hd : ∗ version of Ad ,i.e.(2.2)or(2.3), rather than the original (2.1). A∗ := ~x H ~y A , ~x ~y Z . (2.1) d ∈ d ∀ ∈ d · ∈ For example, A∗ is spanned by 1 , 1 , 2 and 1 , 2 , 1 . 2 3 3 − 3 3 − 3 3 ∗ We shall state two equivalent definitions of Ad scaled up by d + 1 in each dimension. The scale factor ensures that all coordinates will be integers, and these definitions more clearly elucidate the properties of the lattice. ∗ Proposition 2.3. The following two definitions of Ad are equivalent to (2.1) scaled up by d + 1: Figure 2: Construction of A∗ in case d = 2. Left: a regular A∗ := T(~x) ~x (d + 1)Zd+1 , (2.2) d d ∈ grid. The xy-plane is shown in red, along with the unit cube n o in blue. Right: the same figure in orthographic projection, where T is the projection of Rd+1 onto H , namely d seen along the vector~1. Note that many of the points line up, ~ indicating that they will project to the same point on H . The ~x 1 ~ d T :~x ~x · 1. set of projected points form A∗, with appropriate scaling. 7→ − ~1 ~1 ! d · J. Baek, & A. Adams / Some Useful Properties of the Permutohedral Lattice for Gaussian Filtering 3 2.2. Structural Properties In particular, 2 2 As with other lattices, A∗ induces a tessellation of H with its argmin ~y ~x = argmin ∑ (d + 1)zi + k xi d d ~zk − k ~z | − | Voronoi cells. The Voronoi cells of A∗ are polytopes called 2 2 d = argminz ∑(d + 1) zi + 2(d + 1)zi(k xi) permutohedra because they are obtained by permuting the ~ − 2 2 = argmin ∑(d + 1) z 2(d + 1)zixi coordinates of any single vertex.
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