By Takashi Matsuyama, Shohei Nobuhara, Takeshi Takai, Tony Tung
This e-book offers a large assessment of state of the art 3D video creation applied sciences and functions. The textual content opens with a concise creation to the sector, sooner than studying the layout and calibration equipment for multi-view digicam platforms, together with functional implementation applied sciences. quite a number algorithms are then defined for generating 3D video from video info. a variety of 3D video purposes also are proven. positive aspects: describes real-time synchronized multi-view video catch, and item monitoring with a gaggle of energetic cameras; discusses geometric and photometric digital camera calibration, and 3D video studio layout with energetic cameras; examines 3D form and movement reconstruction, texture mapping and snapshot rendering, and lights setting estimation; demonstrates beautiful 3D visualization, visible contents research and modifying, 3D physique motion research, and information compression; highlights the rest demanding situations and the intriguing avenues for destiny learn in 3D video technology.
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Additional info for 3D Video and Its Applications
In practice, however, it requires the exact pan/tilt/zoom values at each image acquisition timing, and only allows prefixed zooming factors. While the pan/tilt/dolly control modifies positions and postures of a camera, the zoom control modifies all of intrinsic, lens distortion, and extrinsic parameters. Moreover, complex optical mechanisms are employed to control zooming, and their accurate mathematical modeling itself is rather difficult. 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.
Private communication with Profs. Takeo Kanade and Yaser Sheikh, Robotics Institute, Carnegie Mellon University, PA (2011) 6. : Automatic camera recovery for closed or open image sequences. In: Proc. of European Conference on Computer Vision, pp. 311–326 (1998) 7. : Colour constancy from hyper-spectral data. In: Proc. of British Machine Vision Conference (2000) 8. : Joint 3D-reconstruction and background separation in multiple views using graph cuts. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp.
11 demonstrates that photometric characteristic variations of uncalibrated cameras can introduce visible artifacts in images rendered from 3D video. Here the simplest view-independent texture generation method in Sect. 3 is used to demonstrate the color differences across original images. Notice that the relative photometric calibration normalizes photometric characteristics of multi-view cameras so that multi-view observations of a 3D surface point give the same pixel intensity value. Hence it does not guarantee that the calibrated color is the “true” color of the object.