Developable Surface Segmentation for CAD Models via Sparse Normal Discontinuity Detection

Segmenting CAD models into developable surface patches is a fundamental problem in geometric modeling and manufacturing-oriented applications. Existing approaches often rely on discrete Gaussian curvature estimation or Gauss map analysis; however, their performance on CAD meshes is frequently hindered by numerical instability, sensitivity to mesh tessellation, and complex parameter tuning. In this work, we propose a simple and robust method for developable surface segmentation based on a sparse normal discontinuity prior. Our key observation is that industrial CAD models are typically composed of large developable regions separated by a sparse set of sharp creases and edges. Consequently, segmentation boundaries correspond to sparse discontinuities in the surface normal field rather than continuous variations in curvature. Based on this perspective, we formulate developable surface segmentation as the detection of sparse normal jump discontinuities. In the discrete setting of triangle meshes, this formulation naturally leads to a dihedral angle-based approach that avoids explicit curvature estimation and admits an efficient graph-based solution. The proposed algorithm consists of face normal computation, dihedral angle-based boundary detection, and connected component extraction on a thresholded face adjacency graph. The method requires only a single geometrically interpretable parameter and naturally aligns segmentation boundaries with sharp features commonly found in CAD models. Experimental results on a diverse set of industrial CAD meshes, including standard benchmarks widely used in related research, demonstrate that the proposed approach achieves robust and accurate segmentation, as validated by both visual coherence and quantitative developability metrics.