Depth Reconstruction from Monocular 2D Images: A Comparative Review of Algorithms and Their Efficacy in 3D Rendering Pipelines

Abstract

Depth reconstruction from 2D images is a critical process for generating realistic 3D visualizations used in applications such as virtual reality (VR), augmented reality (AR), robotics, and autonomous systems. Over the past decade, numerous methods have been developed to estimate depth from 2D data, ranging from traditional geometric approaches to advanced machine learning models. This paper provides a comprehensive survey of these techniques, categorizing them based on methodology, computational efficiency, and application domains. Additionally, the role of depth reconstruction in enhancing 3D rendering quality is discussed. The survey concludes by highlighting current challenges and potential research directions to bridge the gap between academic advancements and real - world implementation.