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3D Video and Its Applications by Takashi Matsuyama, Shohei Nobuhara, Takeshi Takai, Tony Tung

By Takashi Matsuyama, Shohei Nobuhara, Takeshi Takai, Tony Tung

This e-book offers a huge assessment of state of the art 3D video creation applied sciences and purposes. The textual content opens with a concise creation to the sphere, sooner than studying the layout and calibration tools for multi-view digital camera platforms, together with functional implementation applied sciences. a number algorithms are then defined for generating 3D video from video facts. a range of 3D video functions also are validated. positive aspects: describes real-time synchronized multi-view video catch, and item monitoring with a bunch 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 appealing 3D visualization, visible contents research and modifying, 3D physique motion research, and knowledge compression; highlights the rest demanding situations and the interesting avenues for destiny examine in 3D video technology.

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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. Sarkis et al. [15] proposed an example-based calibration method, which interpolates arbitrary intrinsic parameters from their neighboring real measurements based on a moving-least-square approximation.

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. 3 Camera Calibration 39 Fig. 11 Textures generated from Fig. 10(a) and (b), respectively.

3. Even without overlapping areas, the cell-based camera control realizes the continuous object observation, which will be explained in the next section. Camera arrangement: Since the object can move freely on the floor and each camera should observe each cell in higher resolution than specified, a camera ring surrounding the entire floor is a reasonable design of the basic camera arrangement (Fig. 4). Recall that the camera ring is usually augmented with a group of ceiling cameras as shown in Fig.

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