Research and Analysis of 3D Reconstruction Technology under the Influence of Deep Learning

Abstract

As a key technology connecting the physical world and the digital world, 3D reconstruction has wide-ranging applications in fields such as autonomous driving, virtual reality, and industrial inspection. Traditional methods rely on handcrafted features and geometric constraints, and suffer from limitations like poor robustness and low integrity in complex scenarios. In recent years, deep learning technology, with its powerful feature learning and context modeling capabilities, has brought about revolutionary advancements to 3D reconstruction. This paper systematically reviews the research progress of deep learning-driven 3D reconstruction technology, focuses on analyzing core innovations including fusion architectures, attention mechanisms, and lightweight networks, and conducts an in-depth discussion on the breakthroughs and integration trends of cutting-edge representation technologies such as neural implicit fields and 3D Gaussian splatting. Furthermore, the paper points out that current technologies still face challenges such as generalization capability, dynamic scene processing, and computational overhead, and looks forward to future development directions including unified implicit-explicit modeling, lightweight deployment, and general-purpose 3D vision foundation models. It provides important references for in-depth understanding and promotion of the development of 3D reconstruction technology.