Passive 3D Imaging

We describe passive 3D imaging systems that recover 3D information from scenes that are illuminated only with ambient lighting. Although we briefly overview monocular reconstruction, much of the material is concerned with using the geometry of stereo 3D imaging to formulate estimation problems. Firstly, we present an overview of the common techniques used to recover 3D information from camera images. Secondly, we discuss camera modeling and camera calibration as an essential introduction to the geometry of the imaging process and the estimation of geometric parameters. Thirdly, we focus on 3D recovery from multiple views, which can be obtained using multiple cameras at the same time (stereo), or a single moving camera at different times (structure from motion). Epipolar geometry and finding image correspondences associated with the same 3D scene point are two key aspects for such systems, since epipolar geometry establishes the relationship between two camera views, and depth information can be inferred from the correspondences. The details of both stereo and structure from motion, the two essential forms of multiple-view 3D reconstruction technique, are presented. We include a brief overview of the recent trend of applying deep learning to passive 3D imaging. Finally, we present several real-world applications.