Evolutionary multi-objective optimization for mesh simplification of 3D open models
Polygonal surface models are typically used in three dimensional (3D) visualizations and simulations. They are obtained by laser scanners, computer vision systems or medical imaging devices to model highly detailed object surfaces. Surface mesh simplification aims to reduce the number of faces used in a 3D model while keeping the overall shape, boundaries and volume. In this work, we propose to deal with the 3D open model mesh simplification problem from an evolutionary multi-objective viewpoint. The quality of a solution is defined by two conflicting objectives: the accuracy and the simplicity of the model. We adapted the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) to tackle the problem. We compare their performance with two classic approaches and two single-objective implementations. The comparison has been carried out using six different datasets from six corresponding real-world objects. Experimental results have demonstrated that NSGA-II and MOEA/D performs similarly and obtain the best solutions for the studied problem.
evolutionary multi-objective optimization mesh simplification 3d modeling nsga-ii moea/d