Accelerating Evolutionary Construction Tree Extraction via Graph Partitioning

TL;DR

This paper introduces a graph partitioning method to speed up evolutionary algorithms for extracting construction trees from noisy point clouds, significantly reducing computation time while maintaining or improving tree quality.

Contribution

It proposes a novel graph-based search space partitioning scheme that accelerates evolutionary construction tree extraction and leverages parallel CPU capabilities.

Findings

Speed-up up to 46.6 times compared to baseline

Tree sizes increased by 25.2% to 88.6%

Effective acceleration with maintained or improved results

Abstract

Extracting a Construction Tree from potentially noisy point clouds is an important aspect of Reverse Engineering tasks in Computer Aided Design. Solutions based on algorithmic geometry impose constraints on usable model representations (e.g. quadric surfaces only) and noise robustness. Re-formulating the problem as a combinatorial optimization problem and solving it with an Evolutionary Algorithm can mitigate some of these constraints at the cost of increased computational complexity. This paper proposes a graph-based search space partitioning scheme that is able to accelerate Evolutionary Construction Tree extraction while exploiting parallelization capabilities of modern CPUs. The evaluation indicates a speed-up up to a factor of $46.6$ compared to the baseline approach while resulting tree sizes increased by $25.2\%$ to $88.6\%$.