VFS-Net offers vertex- and face-based deep learning pipeline for 3D model segmentation

[TechRxiv] A TechRxiv preprint by Morgan Mekale Smith and co-authors presents VFS-Net, a dual vertex- and face-based deep learning pipeline for segmenting and analyzing dynamic three-dimensional models. According to the authors, the framework showed favorable segmentation and classification metrics on both biological and synthetic 3D model types, indicating broad applicability, and benchmarking across diverse datasets indicated that VFS-Net remained robust to variations in model complexity and mesh density. The authors said traditional machine learning methods require structured grids or volumetric data and are not optimized for data points that vary widely by scale and surface area. The pipeline was designed to operate on 3D models obtained through structured-light scanning, enabling accurate capture of complex surface geometries and topological features. The authors said future extensions will integrate two-dimensional data to broaden the framework's biomedical research relevance.