By Takashi Matsuyama, Shohei Nobuhara, Takeshi Takai, Tony Tung
This publication offers a wide evaluate of state of the art 3D video construction applied sciences and purposes. The textual content opens with a concise creation to the sphere, ahead of analyzing the layout and calibration tools for multi-view digital camera structures, together with useful implementation applied sciences. a number algorithms are then defined for generating 3D video from video info. a variety of 3D video purposes also are established. good points: describes real-time synchronized multi-view video catch, and item monitoring with a bunch of energetic cameras; discusses geometric and photometric digicam calibration, and 3D video studio layout with energetic cameras; examines 3D form and movement reconstruction, texture mapping and photo rendering, and lights surroundings estimation; demonstrates appealing 3D visualization, visible contents research and enhancing, 3D physique motion research, and information compression; highlights the remainder demanding situations and the fascinating avenues for destiny learn in 3D video technology.
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Extra info for 3D Video and Its Applications
Thus we have to develop calibration methods for general pan/tilt/dolly/zoom cameras. There have been proposed two approaches to the active camera calibration: model-based and image-based calibrations. 1 Model-Based Active Camera Calibration There have been proposed advanced studies on modeling and calibration of active cameras [1, 6]. As a realistic pan/tilt/zoom unit modeling, Jain et al.  proposed a calibration method in which the pan and tilt axes can be located at arbitrary positions. In addition, the projection center can move according to zooming.
For example, Lavest et al. [7–9] proposed a method of zoom lens calibration based on a thick lens model rather than a pinhole camera model, and developed a 3D shape reconstruction method by zooming; since zooming shifts the projection center, multi-zooming can realize multi-viewpoint for shape-from-stereo. Sarkis et al.  proposed an example-based calibration method, which interpolates arbitrary intrinsic parameters from their neighboring real measurements based on a moving-least-square approximation.
6. In this section, we assume we can prepare uniform lighting environments for the photometric calibration and present two practical photometric calibration methods for multi-camera systems: relative and absolute methods. The former normalizes photometric characteristics to be shared by all cameras, while the latter establishes their transformations to standard ones defined by reference data. 1 Relative Multi-camera Photometric Calibration A standard idea of gain and vignetting correction is to measure a specified point in the scene by different pixels of an image sensor by moving a camera.