Charting the Landscape of Multi-view Stereo: An In-Depth Exploration of Deep Learning Techniques

Constructing a three-dimensional (3D) representation entails forming a 3D depiction of an object or scene using 2D images or a variety of data sources. It has gained substantial attention due to its versatile applications in virtual reality, augmented reality, medicine, cultural heritage preservation, intelligent transportation, and autonomous driving. Advancements in computational power, deep learning, and sensor technology have markedly improved the quality and efficiency of 3D reconstruction. Modern hardware and software tools have made it more practical. Nevertheless, the interpretability challenges have contributed to the ongoing evolution of deep learning approaches in 3D reconstruction. This paper provides an overview of the latest developments in a prominent 3D reconstruction method, Multi-View Stereo (MVS) deep learning. Notably, the application of 3D reconstruction in security-related scenarios is currently a hot topic. In intelligent transportation and autonomous driving, it enhances traffic safety and navigation accuracy by modeling road and traffic conditions. In medicine, it improves surgical safety through surgical navigation and virtual surgical simulation. In cultural heritage preservation, it aids in digital conservation and artifact security monitoring. The potential of 3D reconstruction in security applications is substantial, promising future research and innovation. In summary, 3D reconstruction holds promise in various application domains, including security. Ongoing advancements in deep learning and related technologies are expected to drive innovative applications in a broader range of security fields.