BIM Home Scene Model Texture Authenticity Reduction Method
For the existing BIM modeling software and methods often produce models with limited and insufficiently realistic textures,which fail to meet the actual needs of different users.This study proposes a method for restoring the authenticity of texture in BIM home scenario models.Firstly,an image dataset is established based on common types of furniture in home scenarios.Then,a neural network algorithm is employed to extract texture features from images,generating similar real furniture images.The corresponding texture maps of different furniture models in the BIM home scenario are obtained by cropping and synthesizing the minimum bounding box.Finally,the restored authenticity of texture in the BIM home scenario model is achieved by providing feedback to the modeling software.Experimental results demonstrate that the BIM home scenario models processed using this method can more realistically recreate realistic scenes.
BIM modeltexture restorationneural networkhome scene
万方数据
维普
