Structural Regularity Detection and Enhancement for Surface Mesh Reconstruction in Reverse Engineering

Abstract

Recovering geometric regularities from scanned mesh models with various types of surface features has always been a challenging task in reverse engineering. To address this problem, this paper presents a regularity detection and enhancement framework for surface mesh reconstruction. Initially, mesh segments are identified by decomposing the original model into planar, quadric and freeform surface features. Similar segments are aligned with each other by pairwise registration and symmetry patterns are detected from the accumulated affine transformations using an improved grid fitting method. Regular relations between symmetry patterns and individual segments are enumerated and strengthened progressively by orientation, dimension and placement optimizations. Finally, the resultant model with enhanced regularities and alleviated noise is obtained by projecting mesh segments onto the optimized parametric surfaces iteratively. Comparative experiments on test models prove that the proposed method outperforms existing methods in recovering both lower- and higher-level regularities of engineering models, especially those with freeform surfaces.

Keywords: egularity detection, constrained surface fitting, grid fitting, surface-level partitioning, mesh registration