This paper provides a comprehensive review of vision-based 3D reconstruction methods, categorizing them into traditional static, dynamic, and machine learning approaches. It highlights the importance of 3D reconstruction in various fields such as computer vision, artificial intelligence, unmanned systems, human-computer interaction, virtual reality, and medicine. The paper discusses the evolution of 3D reconstruction techniques, from early methods like photogrammetry to modern deep learning approaches. It covers explicit and implicit expression methods, including point clouds, voxels, meshes, and implicit surfaces. The paper also delves into active and passive 3D reconstruction methods, such as laser scanning, CT scanning, structured light, TOF, photometric stereo, multi-sensor fusion, texture mapping, shape from focus, binocular stereo vision, and structure from motion (SFM). Additionally, it explores camera calibration, image feature detection, image segmentation, and rendering techniques. The paper aims to provide a detailed overview of the current state of 3D reconstruction, discuss its applications, and address future challenges and trends in the field.This paper provides a comprehensive review of vision-based 3D reconstruction methods, categorizing them into traditional static, dynamic, and machine learning approaches. It highlights the importance of 3D reconstruction in various fields such as computer vision, artificial intelligence, unmanned systems, human-computer interaction, virtual reality, and medicine. The paper discusses the evolution of 3D reconstruction techniques, from early methods like photogrammetry to modern deep learning approaches. It covers explicit and implicit expression methods, including point clouds, voxels, meshes, and implicit surfaces. The paper also delves into active and passive 3D reconstruction methods, such as laser scanning, CT scanning, structured light, TOF, photometric stereo, multi-sensor fusion, texture mapping, shape from focus, binocular stereo vision, and structure from motion (SFM). Additionally, it explores camera calibration, image feature detection, image segmentation, and rendering techniques. The paper aims to provide a detailed overview of the current state of 3D reconstruction, discuss its applications, and address future challenges and trends in the field.