PeelMesh: Efficient Interactive Segmentation via Geodesic-Driven Dynamic Topological Updates

Mesh segmentation is a fundamental challenge in digital geometry processing, shape analysis, and geometric modeling. Automated techniques often lack user control for application-specific results, while conventional interactive methods rely on computationally expensive harmonic field-based approaches and operate on individual models, limiting batch processing of similar datasets. We present PeelMesh, an open-source, user-friendly interactive segmentation framework that combines dynamic KD-tree integration with real-time geodesic extraction, addressing computational bottlenecks. By replacing global energy minimization with incremental topological updates, PeelMesh achieves per-model segmentation in under 100 milliseconds on the Princeton Segmentation Benchmark. For batch processing, predefined anatomical landmark configurations with connectivity constraints reduce repetitive manual interventions across structurally similar datasets. Experimental validation on 842 facial models demonstrates efficient extraction of 19 distinct sub-regions with an average execution time of 442.67 ms per model. A modular architecture and Python bindings enable seamless integration into 3D workflows, lowering technical barriers for cross-domain adoption. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.