This paper provides a comprehensive review of vision-based 3D reconstruction methods, categorizing them into traditional static, dynamic, and machine learning approaches. The authors discuss the importance of 3D reconstruction in various fields, including computer vision, artificial intelligence, and applications such as medical imaging, robotics, and virtual reality. The review covers both explicit and implicit representations of 3D data, with explicit methods including point clouds, voxels, and meshes, while implicit methods use functions to describe geometry. The paper also explores active and passive 3D reconstruction techniques, such as laser scanning, CT scanning, structured light, and TOF, as well as passive methods like texture mapping, shape from focus, and binocular stereo vision. Additionally, it discusses structure from motion (SFM) and multi-view stereo (MVS) techniques, along with image processing steps like camera calibration, feature detection, and image segmentation. The review highlights the role of deep learning in 3D reconstruction and discusses challenges and future directions in the field. The authors aim to provide a comprehensive overview for researchers interested in 3D reconstruction, emphasizing the importance of choosing appropriate methods based on specific application requirements.This paper provides a comprehensive review of vision-based 3D reconstruction methods, categorizing them into traditional static, dynamic, and machine learning approaches. The authors discuss the importance of 3D reconstruction in various fields, including computer vision, artificial intelligence, and applications such as medical imaging, robotics, and virtual reality. The review covers both explicit and implicit representations of 3D data, with explicit methods including point clouds, voxels, and meshes, while implicit methods use functions to describe geometry. The paper also explores active and passive 3D reconstruction techniques, such as laser scanning, CT scanning, structured light, and TOF, as well as passive methods like texture mapping, shape from focus, and binocular stereo vision. Additionally, it discusses structure from motion (SFM) and multi-view stereo (MVS) techniques, along with image processing steps like camera calibration, feature detection, and image segmentation. The review highlights the role of deep learning in 3D reconstruction and discusses challenges and future directions in the field. The authors aim to provide a comprehensive overview for researchers interested in 3D reconstruction, emphasizing the importance of choosing appropriate methods based on specific application requirements.