Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering)

Abstract : Leveraging GANs with Depth-Guided Diffusion, this work introduces Neural Depth-GAN Diffusion, a novel and enhanced framework for 2D-to- 3D picture reconstruction utilizing state-of- the-art artificial intelligence algorithms. Combining Generative Adversarial Networks (GANs), diffusion models, and monocular depth map estimation guarantees computational efficiency by means of which major challenges in 3D reconstruction including precise depth estimation, effective handling of occluded regions, and preservation of geometric consistency across complex structures are addressed. Extensive research on well-known datasets as ShapeNet and KITTI indicates how better our approach is than present state-of- the-art alternatives. Neural Depth-GAN Diffusion not only saves a lot of processing time but also remarkably excels across frequently utilized metrics including Peak Signal-to- Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Chamfer Distance (CD). These findings show how successfully our paradigm strikes a mix between quality and efficiency. Moreover, the flexibility of the proposed method qualifies it for a broad spectrum of pragmatic applications including augmented reality (AR), autonomous driving, and medical imaging. By enhancing accuracy and computing feasibility, Neural Depth-GAN Diffusion sets new benchmarks in artificial intelligence-driven image processing across various domains by so giving a transformative answer to 2D-to- 3D reconstruction issues.