Geodesic isolines derived from polylines constitute a crucial element within geographic information systems, playing a pivotal role in enhancing the understanding of geographical terrains. Current methods for delineating isolines sourced from polylines on discrete meshes often rely on simplistic linear interpolation. However, these methods fall short in accuracy due to the complex, non-linear nature of geodesic distance fields, thereby inadequately capturing intricate topological features present in real isolines. To tackle this challenge, we demonstrate that Apollonius diagrams can effectively encode the geometric attributes of isolines on meshes and extract the isolines using the Apollonius diagrams with geodesic metric. Moreover, exact geodesic computation is computationally intensive and memory-demanding. In response, we introduce a graph-based approach enhanced by Steiner point insertion, offering a practical method for computing geodesic distances. Drawing on these strategies, we introduce an accurate and efficient algorithm for polyline-sourced isoline computation on triangle meshes. Comprehensive evaluations indicate that our approach yields significantly more accurate geodesic isolines compared to the commonly employed linear interpolation.

The computation of polyline-sourced geodesic offset holds significant importance in a variety of applications, including but not limited to solid modeling, tool path generation for computer numerical control (CNC) machining, and parametrization. The traditional approaches for geodesic offsets have typically relied on the availability of an exact geodesic metric. Nevertheless, the computation of exact geodesics is characterized by its time-consuming nature and substantial memory usage. To tackle the limitation, our study puts forward a novel approach that seeks to circumvent the reliance on exact geodesic metrics. The proposed method entails a reformulated graph method that incorporates Steiner point insertion, serving as an effective solution for obtaining geodesic distances. By leveraging the aforementioned strategies, we present an efficient and robust algorithm designed for the computation of polyline-sourced geodesic offsets. The experimental evaluation, conducted on a diverse set of three-dimensional models, demonstrates significant improvements in computational speed and memory requirements compared to established state-of-the-art methods.