Vertex Deletion for 3D Delaunay Triangulations Kevin Buchin

Olivier Devillers

Wolfgang Mulzer

Okke Schrijvers

Jonathan Shewchuk

TU Eindhoven

INRIA Sophia Antipolis

Freie Universität Berlin

Stanford University

UC Berkeley

[email protected]

[email protected]

[email protected]

DELAUNAY TRIANGULATIONS

[email protected]

[email protected]

TRIANGULATE AND SEW

The Delaunay triangulation (DT) of a point set is a triangulation of the convex hull such that the circumcircle of each triangle contains no other points (Fig. 1). It is a classic structure in Computational Geometry and is used for instance for interpolation in Graphics and Scientific Computing. We focus on deletions: given 𝐷𝑇(𝑆) and a point Fig. 1. The Delaunay triangulation of a point set 𝑆. The circum𝑞 ∈ 𝑆, find 𝐷𝑇(𝑆 ∖ 𝑞). In 2D, there exist both 𝐷𝑇(𝑆) circle of each triangle is empty. theoretically and practically fast algorithms. The best known 3D algorithm runs in 𝑂(𝑑 log 𝑑 + 𝐶 𝑃 ) with 𝑑 = deg(𝑞), 𝑃 the set of incident vertices and 𝐶(𝑃) is the structural cost of construction with a RIC. We reduce this to 𝑂(𝑑 + 𝐶 ⊗ 𝑃 ) with O(𝐶 ⊗ 𝑃 ) ≤ 𝑂(𝐶 𝑃 ).

In previous work, “Triangulate and Sew” retriangulates the vertices incident to 𝑞 and sews this result into the original triangulation. This process is shown in Fig. 2. We reduce the point location time for the retriangulation by using information of the connectivity in the original triangulation. We reduce the structural complexity by identifying and preventing the creation of simplices that would be discarded when sewed back into the triangulation.

incident triangles incident vertices

triangulat e

“outer” triangles

“inner” triangles

Fig. 2. Approach for deletions.

ALGORITHM We apply the “reverse deletion trick” to delete point 𝑞. On top, we remove points one-by-one in the lower-dimensional Link DT: 𝐷𝑇 ℓ (𝑄) , storing guides in the process. We then reconstruct using the guides and the Conflict DT: 𝐷𝑇𝑞⊗ 𝑃 , preventing unnecessary simplices from being created.

SAMPLING AND GUIDES By using a guide when we insert point 𝑝𝑖 , we reduce the time needed for point location. By picking a point that will be a neighbor in the new triangulation, we can charge walking to the structural complexity. If the guide has constant degree, then finding a simplex in conflict with 𝑝𝑖 takes constant time. We use several sampling schemes that find low degree guides: • Random low-degree edge • Random triangle created during removal • BRIO + Bounded Degree Spanning Tree Using 𝐶 ⊗ 𝑃 to denote the structural change induced by a randomized incremental construction of 𝑃, we have the following theorem: Thm 1. Our algorithm runs in 𝑂(𝐶 ⊗ 𝑃 ) expected time using i. Uniform sampling using triangle guides ii. BRIO sampling using vertex guides

Fig. 3. The overview of how we delete points from a Delaunay triangulation.

RESULTS

MANAGING BOUNDARIES During the deletions in the Link DT and insertions in the Conflict DT we use alternative geometric predicates.

We implemented the different sampling schemes for finding a low degree guide and compared our implementation with CGAL.

When deleting points from a 3D DT, the Link DT is a DT on a 2-dimensional topological sphere. We include the point 𝑞 in each incircle predicate -turning it into an in-sphere predicate- to ensure correct behavior.

All of our implementations compare favorably (see Fig. 4). When deleting lowdegree points we require similar time while high degree points are a lot faster.

When inserting points in the Conflict DT, we want to prevent the creation of simplices that are not in conflict with 𝑞. We apply an alternative in-sphere predicate for boundary simplices (containing the special q-boundary vertex: 𝑞𝜕 ). We define the geometric predicate 𝑖𝑛𝑠𝑝ℎ𝑒𝑟𝑒 ⊗ 𝑎, 𝑏, 𝑐, 𝑞𝜕 , 𝑤 as 𝑖𝑛𝑠𝑝ℎ𝑒𝑟𝑒 ⊗ 𝑏, 𝑎, 𝑐, 𝑑, 𝑤 , where 𝑑 is the vertex on the other side of triangle 𝑎, 𝑏, 𝑐. We can see the effect in Fig. 3 when 𝑝5 is inserted: the boundary triangle 𝑝2 , 𝑝4 , 𝑞𝜕 is found to be in conflict with 𝑝5 and is replaced while boundary triangle p1 , 𝑝2 , 𝑞𝜕 is not.

Both using guides, and using the Conflict DT reduce running time, with the largest benefit coming from the Conflict DT.

Fig. 4.

The running time of our algorithm with different sampling schemes, compared to CGAL.

Vertex Deletion for 3D Delaunay Triangulations

constant degree, then finding a simplex in ... BRIO + Bounded Degree Spanning Tree. Using. ⊗ ... The best known 3D algorithm runs in ( log + ) with = deg( ),.

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