Abstract

With the development of hardware equipment and theoretical knowledge, 3D reconstruction technology has played a key role in many fields such as industrial manufacturing, cultural relics protection and augmented reality, and has attracted great attention in the field of computer vision. 3D reconstruction methods can be divided into traditional methods and deep learning methods. Among them, the traditional method technology is mature and widely used at present, while the 3D reconstruction method based on deep learning is developing rapidly, which has the advantages of low dependence on equipment, strong method generalization and strong adaptability to the environment. In order to promote the development of subsequent research, this paper first gives a comprehensive classification and review of the existing 3D reconstruction methods, then analyzes their principles and performance, and finally further analyzes the existing problems and challenges, and explores possible future research directions, with the aim of providing new ideas for more in depth 3D reconstruction tasks.

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