A Flexible Pipeline for the Optimization of CSG Trees

TL;DR

This paper introduces a flexible pipeline for optimizing CSG trees, focusing on reducing complexity and enhancing editability, by combining redundancy removal, decomposition, and meta-heuristics, with a new measure for tree editability.

Contribution

It presents a systematic comparison of CSG tree optimization methods and proposes a novel pipeline that improves tree simplicity and editability using a new quantitative measure.

Findings

The pipeline effectively reduces CSG tree complexity.

The new editability measure guides optimization constraints.

Meta-heuristics improve the quality of optimized trees.

Abstract

CSG trees are an intuitive, yet powerful technique for the representation of geometry using a combination of Boolean set-operations and geometric primitives. In general, there exists an infinite number of trees all describing the same 3D solid. However, some trees are optimal regarding the number of used operations, their shape or other attributes, like their suitability for intuitive, human-controlled editing. In this paper, we present a systematic comparison of newly developed and existing tree optimization methods and propose a flexible processing pipeline with a focus on tree editability. The pipeline uses a redundancy removal and decomposition stage for complexity reduction and different (meta-)heuristics for remaining tree optimization. We also introduce a new quantitative measure for CSG tree editability and show how it can be used as a constraint in the optimization process.