Semialgebraic Range Stabbing, Ray Shooting, and Intersection Counting in the Plane

Timothy M. Chan, Pingan Cheng, Da Wei Zheng
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Abstract

Polynomial partitioning techniques have recently led to improved geometric data structures for a variety of fundamental problems related to semialgebraic range searching and intersection searching in 3D and higher dimensions (e.g., see [Agarwal, Aronov, Ezra, and Zahl, SoCG 2019; Ezra and Sharir, SoCG 2021; Agarwal, Aronov, Ezra, Katz, and Sharir, SoCG 2022]). They have also led to improved algorithms for offline versions of semialgebraic range searching in 2D, via lens-cutting [Sharir and Zahl (2017)]. In this paper, we show that these techniques can yield new data structures for a number of other 2D problems even for online queries: 1. Semialgebraic range stabbing. We present a data structure for $n$ semialgebraic ranges in 2D of constant description complexity with $O(n^{3/2+\varepsilon})$ preprocessing time and space, so that we can count the number of ranges containing a query point in $O(n^{1/4+\varepsilon})$ time, for an arbitrarily small constant $\varepsilon>0$. 2. Ray shooting amid algebraic arcs. We present a data structure for $n$ algebraic arcs in 2D of constant description complexity with $O(n^{3/2+\varepsilon})$ preprocessing time and space, so that we can find the first arc hit by a query (straight-line) ray in $O(n^{1/4+\varepsilon})$ time. 3. Intersection counting amid algebraic arcs. We present a data structure for $n$ algebraic arcs in 2D of constant description complexity with $O(n^{3/2+\varepsilon})$ preprocessing time and space, so that we can count the number of intersection points with a query algebraic arc of constant description complexity in $O(n^{1/2+\varepsilon})$ time. In particular, this implies an $O(n^{3/2+\varepsilon})$-time algorithm for counting intersections between two sets of $n$ algebraic arcs in 2D.
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半代数平面范围刺击、射线射击和交点计数
多项式分割技术最近为三维和更高维度中与半代数范围搜索和交集搜索相关的各种基本问题带来了改进的几何数据结构(例如,参见[Agarwal、Aronov、Ezra 和 Zahl,SoCG 2019;Ezra 和 Sharir,SoCG 2021;Agarwal、Aronov、Ezra、Katz 和 Sharir,SoCG 2022])。他们还通过透镜切分[Sharir and Zahl (2017)],改进了 2D 半代数范围搜索的离线版本算法。在本文中,我们将展示这些技术可以为其他一些二维问题产生新的数据结构,甚至可以用于在线查询:1.半代数范围刺探。我们提出了一种描述复杂度恒定、预处理时间和空间均为$O(n^{3/2+\varepsilon})$的二维中$n$半代数范围的数据结构,因此,对于任意小的常数$\varepsilon>0$,我们可以在$O(n^{1/4+\varepsilon})$时间内计算包含查询点的范围数量。2.代数弧中的光线射击我们提出了一种描述复杂度恒定、预处理时间和空间均为 $O(n^{3/2+\varepsilon})$的 2D 中 $n$ 代数弧的数据结构,因此我们可以在 $O(n^{1/4+\varepsilon})$时间内找到查询(直线)射线命中的第一个弧。3.代数弧中的交点计数。我们为二维中具有恒定描述复杂度的$n$代数弧提出了一种数据结构,其预处理时间和空间为$O(n^{3/2+\varepsilon})$,因此我们可以在$O(n^{1/2+\varepsilon})$时间内计算与具有恒定描述复杂度的查询代数弧的交点数量。特别是,这意味着可以用 $O(n^{3/2+\varepsilon})$ 时间的算法来计算二维中两组 $n$ 代数弧之间的交点。
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