TransUNet Image 3D Reconstruction with Hyperparameter Optimization

Ancient architecture is characterized by its complexity and exquisite structure, but most existing images are in 2D format.This study proposes a TransUNet-based 3D reconstruction method with hyperparameter optimization for depth prediction to enhance the effectiveness, accuracy, and efficiency of reconstructing ancient buildings from 2D images.The method employs Restricted Boltzmann Machine (RBM) for depth prediction and an optimized ant colony algorithm for network parameter optimization.Experiments demonstrate that the proposed method achieves an average F1 score of 96.8% in reconstructing ancient buildings, outperforming other algorithms in terms of processing time and efficiency.The results validate the superiority of the proposed algorithm in processing images of ancient architecture, improving measurement accuracy and reducing execution time.This study contributes to the digitization and preservation of cultural heritage.