A review of 3D reconstruction methods based on deep learning

Abstract

In computer vision, an important research area is three-dimensional reconstruction. Using computer technology to reconstruct three-dimensional models of objects has become an indispensable part of in-depth research in many fields. This thesis presents the development process of 3D reconstruction methods that use deep learning. Compared with traditional methods, the 3D reconstruction method based on deep learning has more flexible input and output and higher efficiency. This thesis classifies the methods by the type of 3D model representation and discusses different frameworks for 3D reconstruction based on deep learning. With the introduction of the method NeRF (Neural Radiance Field), the three-dimensional reconstruction work based on deep learning has got a great development. NeRF can achieve good results in a very short period of time in the face of various complex scenes. With the continuous improvement of NeRF by researchers, this method has achieved more amazing results. Finally, the existing problems in the field of 3D reconstruction, the causes of problems and possible solutions are analyzed. Finally, the future development trend and direction of this field are hypothesized and discussed.