Point-in-Convex Polygon and Point-in-Convex Polyhedron Algorithms with O(1) Complexity using Space Subdivision

TL;DR

This paper introduces new algorithms for point-in-convex polygon and polyhedron tests that achieve constant time complexity through space subdivision, improving speed over traditional methods.

Contribution

The paper presents novel point-in-convex polygon and polyhedron algorithms with O(1) runtime using space subdivision, a significant improvement over existing O(N) or O(lg N) methods.

Findings

Algorithms achieve O(1) query time after preprocessing.

Approach is simple to implement and applicable to dual problems.

Method leverages space subdivision for convex shapes in E3.

Abstract

There are many space subdivision and space partitioning techniques used in many algorithms to speed up computations. They mostly rely on orthogonal space subdivision, resp. using hierarchical data structures, e.g. BSP trees, quadtrees, octrees, kd-trees, bounding volume hierarchies, etc. However in some applications a non-orthogonal space subdivision can offer new ways for actual speed up. In the case of convex polygon in E3 a simple Point-in-Polygon test is of the O(N) complexity and the optimal algorithm is of O(lg N) computational complexity. In the E3 case, the complexity is O(N) even for the convex polyhedron as no ordering is defined. New Point-in-Convex Polygon and Point-in-Convex Polyhedron algorithms are presented based on space subdivision in the preprocessing stage resulting to O(1) run-time complexity. The presented approach is simple to implement. Due to the principle of duality, dual problems, e.g. line-convex polygon, line clipping, can be solved in a similarly.