3D Image Reconstruction Using Virtual Reality and Multimodal Deep Learning

Abstract

Three-dimensional (3D) image reconstruction techniques have found extensive applications in fields such as medicine, education, and computer science, enabling high-precision 3D images to enhance work efficiency. However, traditional methods of 3D image reconstruction solely rely on acoustic information, resulting in limited accuracy. Therefore, a novel method based on virtual reality (VR) and multimodal deep learning is proposed for 3D image reconstruction. First, VR technology is employed to capture 3D image information, followed by de-noising and removal of redundant information. Second, a logarithmic transformation method is employed to enhance the details in the 3D image. Finally, a multimodal deep learning method is utilized to reconstruct the 3D image from the perspectives of imagery, sound, and video. Experimental results demonstrate that the proposed method achieves superior 3D image reconstruction with an accuracy of over 90%. The reconstruction process is efficient and exhibits low signal-to-noise ratio, while the average registration error is less than 0.04%. These findings highlight the practical value and potential applications of the proposed method.