Abstract
Three-dimensional dense reconstruction involves extracting the full shape and texture details of three-dimensional objects from two-dimensional images. Although 3D reconstruction is a crucial and well-researched area, it remains an unsolved challenge in dynamic or complex environments. This work provides a comprehensive overview of classical 3D dense reconstruction techniques, including those based on geometric and optical models, as well as approaches leveraging deep learning. It also discusses the datasets used for deep learning and evaluates the performance and the strengths and limitations of deep learning methods on these datasets.