Hybrid Shape Descriptor Fusion with LightGBM for Robust 3D Mesh Classification and Retrieval

Recent advances in 3D mesh acquisition and the development of interactive modeling tools have significantly increased both the quantity and diversity of available 3D model databases. Therefore, the task of searching, querying, and re-trieving models in large-scale 3D databases has become a focus of research in this area. Indexing 3D models for content-based retrieval is a challenging task that involves numerous algorithms and tools to capture the most significant representation of the object. In this study, a novel framework for 3D mesh retrieval is proposed that combines distribution-based, spectral, and geometric features into a single representation and employs a machine learning classifier based on LightGBM (Light Gradient Boosting Machine) for classifying 3D objects. To capture the complex geometry of 3D meshes, our approach analyzes surface smoothness, radial vertex distributions, spectral signatures, global shape distributions, topological connectivity, and local curvatures. Evaluated on the Princeton Shape Benchmark (PSB), the pro-posed approach achieves a 1st Tier accuracy of 0.97 and an F-Measure of 0.96, substantially outperforming both individual descriptors and state-of-the-art methods. The mean pairwise cross-correlation between descriptors is low (?¯ = 0.128), confirming their complementary rather than redundant nature. The proposed approach presents a consistent solution with potential applications in various areas, such as computer vision, robotics, e-commerce, medical imaging, and other related fields.