Building Information Modeling (BIM) three-dimensional (3D) objects undergo repeated conversion and reconstruction processes for cross-platform utilization, during which geometric information loss, topological distortion, and semantic omission frequently occur, leading to fundamental limitations in accurate shape reconstruction and semantic-based functional reuse. The academic objective of this study is to overcome these limitations by proposing a three-stage sequential cross-platform reconstruction framework, consisting of semantic-vertex-based functional utilization, semantic-vertex-based invariant triangle mesh reconstruction, and semantic-vertex-based functional reuse, and to experimentally validate its effectiveness. To this end, an FBX–JSON dual-pipeline-based data management architecture is introduced to process visual geometric data and non-visual semantic metadata in parallel, thereby ensuring platform independence and data consistency. Experimental validation was conducted using IFC-based BIM objects generated in Autodesk Revit and triangle mesh models processed in Blender, at both the object and project levels. Quantitative evaluation was performed using geometric identity preservation, mesh completeness, semantic vertex restoration accuracy, and functional retention rate as the core performance indicators. The results reveal that the primary cause of mesh failure during platform transformation is face normal inconsistency, which can be stably resolved through auxiliary remeshing, thereby ensuring robust mesh reconstruction. Although the experiments were limited to round-trip transfers between Blender and Unity, the results experimentally verify the effectiveness of the proposed three-stage reconstruction framework and dual-pipeline data architecture, while also demonstrating their strong potential for generalization to broader cross-platform BIM environments.
Jaeho Cho (Mon,) studied this question.