Emergent Mind

Abstract

Let $\mathcal{T}$ be a set of $n$ flat (planar) semi-algebraic regions in $\mathbb{R}3$ of constant complexity (e.g., triangles, disks), which we call plates. We wish to preprocess $\mathcal{T}$ into a data structure so that for a query object $\gamma$, which is also a plate, we can quickly answer various intersection queries, such as detecting whether $\gamma$ intersects any plate of $\mathcal{T}$, reporting all the plates intersected by $\gamma$, or counting them. We also consider two simpler cases of this general setting: (i) the input objects are plates and the query objects are constant-degree parametrized algebraic arcs in $\mathbb{R}3$ (arcs, for short), or (ii) the input objects are arcs and the query objects are plates in $\mathbb{R}3$. Besides being interesting in their own right, the data structures for these two special cases form the building blocks for handling the general case. By combining the polynomial-partitioning technique with additional tools from real algebraic geometry, we present many different data structures for intersection queries, which also provide trade-offs between their size and query time. For example, if $\mathcal{T}$ is a set of plates and the query objects are algebraic arcs, we obtain a data structure that uses $O*(n{4/3})$ storage (where the $O*(\cdot)$ notation hides subpolynomial factors) and answers an arc-intersection query in $O*(n{2/3})$ time. This result is significant since the exponents do not depend on the specific shape of the input and query objects. For a parameter $s\in [n{4/3}, n{t_Q}]$ where $tQ\ge 3$ is the number of real parameters needed to specify a query arc, the query time can be decreased to $O*((n/s{1/tQ}){\tfrac{2}{3}(1-1/t_Q)})$ by increasing the storage to $O*(s)$.

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