You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Let @math be a point in a polygonal domain @math of @math holes and @math vertices. We consider a quickest visibility query problem. Given a query point @math in @math , the goal is to find a shortest path in @math to move from @math to see @math as quickly as possible. Previously, (SoCG 2015) built a data structure of size @math that can answer each query in @math time, where @math is the inverse Ackermann function and @math is the size of the visibility polygon of @math in @math (and @math can be @math in the worst case). In this paper, we present a new data structure of size @math that can answer each query in @math time. Our result improves the previous work when @math is relatively small. In particular, if @math is a constant, then our result even matches the best result for the simple polygon case (i.e., @math ), which is optimal. As a by-product, we also have a new algorithm for a shortest-path-to-segment query problem. Given a query line segment @math in @math , the query seeks a shortest path from @math to all points of @math . Previously, gave a data structure of size @math that can answer each query in @math time, and another data structure of size @math with @math query time. We present a data structure of size @math with query time @math , which also favors small values of @math and is optimal when @math .

Cited Abstract(s):
[1]: In this paper, we study the problem of finding the shortest path from a given source point in a simple polygon to some point visible from a given query point. We will present an algorithm based on the notion of funnels in simple polygons. The algorithm preprocesses the input containing a simple polygon and a source point to produce a data structure to answer the queries in logarithmic time. The time and space required for preprocessing is quadratic in size of the simple polygon.

[2]: 

[3]: We show how to preprocess a polygonal domain with a fixed starting point s in order to answer efficiently the following queries: Given a point q, how should one move from s in order to see q as soon as possible? This query resembles the well-known shortest-path-to-a-point query, except that the latter asks for the fastest way to reach q, instead of seeing it. Our solution methods include a data structure for a different generalization of shortest-path-to-a-point queries, which may be of independent interest: to report efficiently a shortest path from s to a query segment in the domain.
In the simple polygon case (i.e., @math ), better results are possible for both the quickest visibility queries and the segment queries. For the quickest visibility queries, Khosravi and Ghodsi [1] first proposed a data structure of @math size that can answer each query in @math time. [3] gave an improved result and they built a data structure of @math size in @math time, with @math query time. For the segment queries, [3] built a data structure of @math size in @math time, with @math query time. Chiang and Tamassia [2] achieved the same result for the segment queries and they also gave some more general results (e.g., when the query is a convex polygon).