Accelerating Evolutionary Construction Tree Extraction via Graph Partitioning
This work addresses computational bottlenecks in reverse engineering for computer-aided design, offering a significant speed improvement but with increased tree sizes, making it an incremental advancement.
The paper tackles the problem of accelerating evolutionary construction tree extraction from noisy point clouds in reverse engineering by proposing a graph-based search space partitioning scheme, achieving a speed-up of up to 46.6 times compared to the baseline while increasing tree sizes by 25.2% to 88.6%.
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 $88.6\%$.