Visibility Queries in Simple Polygons
For computational geometry researchers, this work provides more efficient solutions to a fundamental query problem, with incremental improvements over existing bounds.
This paper presents new data structures for visibility queries in simple polygons, achieving improved space-time tradeoffs. For example, they reduce space from O(n^3) to O(n^{2+ε}) for O(log n + k) queries, and improve query time from O(log^2 n + k) to O(log n log log n + k) with O(n^2) space.
Given a simple polygon $P$ with $n$ vertices, we consider the problem of constructing a data structure for visibility queries: for any query point $q \in P$, compute the visibility polygon of $q$ in $P$. To obtain $O(\log n + k)$ query time, where $k$ is the size of the visibility polygon of $q$, the previous best result requires $O(n^3)$ space. In this paper, we propose a new data structure that uses $O(n^{2+ε})$ space, for any $ε> 0$, while achieving the same query time. If only $O(n^2)$ space is available, the best known result provides $O(\log^2 n + k)$ query time. We improve this to $O(\log n \log \log n + k)$ time. When restricted to $o(n^2)$ space, the only previously known approach, aside from the $O(n)$-time algorithm that computes the visibility polygon without preprocessing, is an $O(n)$-space data structure that supports $O(k \log n)$-time queries. We construct a data structure using $O(n \log n)$ space that answers visibility queries in $O(n^{1/2+ε} + k)$ time. In addition, for the special case in which $q$ lies on the boundary of $P$, we build a data structure of $O(n \log n)$ space supporting $O(\log^2 n + k)$ query time; alternatively, we achieve $O(\log n + k)$ query time using $O(n^{1+ε})$ space. To achieve our results, we propose a new method for decomposing simple polygons, which may be of independent interest.