3D Reconstruction of Optical Building Images Based on Improved 3D-R2N2 Algorithm

Abstract

Three-dimensional reconstruction technology is a key element in the construction of urban geospatial models. Addressing the current shortcomings in reconstruction accuracy, registration results convergence, reconstruction effectiveness, and convergence time of 3D reconstruction algorithms, we propose an optical building object 3D reconstruction method based on an improved 3D-R2N2 algorithm. The method inputs preprocessed optical remote sensing images into a Convolutional Neural Network (CNN) with dense connections for encoding, converting them into a low-dimensional feature matrix and adding a residual connection between every two convolutional layers to enhance network depth. Subsequently, 3D Long Short-Term Memory (3D-LSTM) units are used for transitional connections and cyclic learning. Each unit selectively adjusts or maintains its state, accepting feature vectors computed by the encoder. These data are further passed into a Deep Convolutional Neural Network (DCNN), where each 3D-LSTM hidden unit partially reconstructs output voxels. The DCNN convolutional layer employs an equally sized 3 3 3 convolutional kernel to process these feature data and decode them, thereby accomplishing the 3D reconstruction of buildings. Simultaneously, a pyramid pooling layer is introduced between the feature extraction module and the fully connected layer to enhance the performance of the algorithm. Experimental results indicate that, compared to the 3D-R2N2 algorithm, the SFM-enhanced AKAZE algorithm, the AISI-BIM algorithm, and the improved PMVS algorithm, the proposed algorithm improves the reconstruction effect by 5.3%, 7.8%, 7.4%, and 1.0% respectively. Furthermore, compared to other algorithms, the proposed algorithm exhibits higher efficiency in terms of registration result convergence and reconstruction time, with faster computational speed. This research contributes to the enhancement of building 3D reconstruction technology, laying a foundation for future research in deep learning applications in the architectural field