3D mesh simplification with feature preservation based on Whale Optimization Algorithm and Differential Evolution

Yaqian Liang

doi:10.3233/ICA-200641

2020-09-11

113

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Yaqian Liang

Fazhi He *

Xian‐Tao Zeng

DOI：10.3233/ICA-200641

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摘要

En 中文

Large-scale 3D models consume large computing and storage resources. To address this challenging problem, this paper proposes a new method to obtain the optimal simplified 3D mesh models with the minimum approximation error. First, we propose a feature-preservation edge collapse operation to maintain the feature edges, in which the collapsing cost is calculated in a novel way by combining Gauss curvature and Quadratic Error Metrics (QEM). Second, we introduce the edge splitting operation into the mesh simplification process and propose a hybrid 'undo/redo' mechanism that combines the edge splitting and edge collapse operation to reduce the number of long and narrow triangles. Third, the proposed 'undo/redo' mechanism can also reduce the approximation error; however, it is impossible to manually choose the best operation sequence combination that can result in the minimum approximation error. To solve this problem, we formulate the proposed mesh simplification process as an optimization model, in which the solution space is composed of the possible combinations of operation sequences, and the optimization objective is the minimum of the approximation error. Finally, we propose a novel optimization algorithm, WOA-DE, by replacing the exploration phase of the original Whale Optimization Algorithm (WOA) with the mutate and crossover operations of Differential Evolution (DE) to compute the optimal simplified mesh model more efficiently. We conduct numerous experiments to test the capabilities of the proposed method, and the experimental results show that our method outperforms the previous methods in terms of the geometric feature preservation, triangle quality, and approximation error.

Keyword:

3D mesh simplification

geometric feature preservation

'undo/redo' mechanism

objective optimization