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The surge in 3D modeling has led to a pronounced research emphasis on thefield of 3D shape retrieval. Numerous contemporary approaches have been putforth to tackle this intricate challenge. Nevertheless, effectively addressingthe intricacies of cross-modal 3D shape retrieval remains a formidableundertaking, owing to inherent modality-based disparities. This study presentsan innovative notion, termed "geometric words", which functions as elementalconstituents for representing entities through combinations. To establish theknowledge graph, we employ geometric words as nodes, connecting them via shapecategories and geometry attributes. Subsequently, we devise a unique graphembedding method for knowledge acquisition. Finally, an effective similaritymeasure is introduced for retrieval purposes. Importantly, each 3D or 2D entitycan anchor its geometric terms within the knowledge graph, thereby serving as alink between cross-domain data. As a result, our approach facilitates multiplecross-domain 3D shape retrieval tasks. We evaluate the proposed method'sperformance on the ModelNet40 and ShapeNetCore55 datasets, encompassingscenarios related to 3D shape retrieval and cross-domain retrieval.Furthermore, we employ the established cross-modal dataset (MI3DOR) to assesscross-modal 3D shape retrieval. The resulting experimental outcomes, inconjunction with comparisons against state-of-the-art techniques, clearlyhighlight the superiority of our approach.

3D Shape Knowledge Graph for Cross-domain 3D Shape Retrieval