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DOI: 10.14569/IJACSA.2026.0170492
PDF

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

Author 1: Khadija Arhid
Author 2: Youness Ghazi
Author 3: Ilham Kachbal
Author 4: Fatima Rafii Zakani
Author 5: Mohcine Bouksim
Author 6: Said El Abdellaoui
Author 7: Taoufiq Gadi
Author 8: Mohamed Aboulfatah

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 17 Issue 4, 2026.

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Abstract: 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.

Keywords: 3D object; 3D mesh retrieval; classification; machine learning; shape matching; feature fusion; LightGBM

Khadija Arhid, Youness Ghazi, Ilham Kachbal, Fatima Rafii Zakani, Mohcine Bouksim, Said El Abdellaoui, Taoufiq Gadi and Mohamed Aboulfatah. “Hybrid Shape Descriptor Fusion with LightGBM for Robust 3D Mesh Classification and Retrieval”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.4 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170492

@article{Arhid2026,
title = {Hybrid Shape Descriptor Fusion with LightGBM for Robust 3D Mesh Classification and Retrieval},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170492},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170492},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {4},
author = {Khadija Arhid and Youness Ghazi and Ilham Kachbal and Fatima Rafii Zakani and Mohcine Bouksim and Said El Abdellaoui and Taoufiq Gadi and Mohamed Aboulfatah}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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