3-D vertical ray shooting and 2-D point enclosure, range searching, and arc shooting amidst convex fat objects

We present a new data structure for a set of n convex simply-shaped fat objects in the plane, and use it to obtain efficient and rather simple solutions to several problems including (i) vertical ray shooting - preprocess a set K of n non-intersecting convex simply-shaped flat objects in 3-space, whose cursive Greek chiy-projections are fat, for efficient vertical ray shooting queries, (ii) point enclosure - preprocess a set C of n convex simply-shaped fat objects in the plane, so that the k objects containing a query point p can be reported efficiently, (iii) bounded-size range searching - preprocess a set C of n convex fat polygons, so that the k objects intersecting a "not-too-large" query polygon can be reported efficiently, and (iv) bounded-size segment shooting - preprocess a set C as in (iii), so that the first object (if exists) hit by a "not-too-long" oriented query segment can be found efficiently. For the first three problems we construct data structures of size O(λ_s(n)log³n), where s is the maximum number of intersections between the boundaries of the (cursive Greek chiy-projections) of any pair of objects, and λ_s(n) is the maximum length of (n, s) Davenport-Schinzel sequences. The data structure for the fourth problem is of size O(λ_s(n)log²n). The query time in the first problem is O(log⁴n), the query time in the second and third problems is O(log³ n + k log² n), and the query time in the fourth problem is O(log³ n). We also present a simple algorithm for computing a depth order for a set K as in (i), that is based on the solution to the vertical ray shooting problem. (A depth order for K, if exists, is a linear order of K, such that, if K₁, K₂ ∈ K and K₁ lies vertically above K₂, then K₁ precedes K₂.) Unlike the algorithm of Agarwal et al. (1995) that might output a false order when a depth order does not exist, the new algorithm is able to determine whether such an order exists, and it is often more efficient in practical situations than the former algorithm.