ScholarWorks@동국대학교

초록

Generative AI has established image-driven 3D modeling as a vital tool for product prototyping. This study investigated the intelligent segmentation function provided by the Tripo platform, with a focus on semantic recognition, component partitioning, and geometric structure refinement. A standardized assessment framework was applied to three artifacts of varying complexity (i.e., a stool, robot, and car) to evaluate modeling efficiency, segmentation accuracy, and platform compatibility both quantitatively and qualitatively. The results showed that Tripo’s AI-driven completion mechanism effectively bridges visual generation with structural understanding, producing 3D models with clear component hierarchies. Furthermore, comparative analysis with traditional workflows revealed that Tripo provides distinct advantages in component editability and design iteration, thereby validating its practical utility in digital prototyping.

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