By Takashi Matsuyama, Shohei Nobuhara, Takeshi Takai, Tony Tung
This booklet provides a vast overview of cutting-edge 3D video creation applied sciences and functions. The textual content opens with a concise creation to the sector, ahead of analyzing the layout and calibration tools for multi-view digicam structures, together with useful implementation applied sciences. a variety of algorithms are then defined for generating 3D video from video information. a range of 3D video purposes also are verified. positive factors: describes real-time synchronized multi-view video trap, 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 photograph rendering, and lighting fixtures setting estimation; demonstrates beautiful 3D visualization, visible contents research and enhancing, 3D physique motion research, and knowledge 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
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.  proposed an example-based calibration method, which interpolates arbitrary intrinsic parameters from their neighboring real measurements based on a moving-least-square approximation.
In , we developed a more sophisticated camera control method which maximizes the object observability and reduces the idle cameras. Even without knowledge about the object trajectory, we can reduce the number of the idle cameras by assuming the cameras can be controlled from one cell to another faster than the object motion. Suppose the object in cell A is approaching to the corner shared with cells B and C as illustrated with x in Fig. 3. 3 Algorithm Implementation 55 observation. If the object changes its motion and approaches the corner shared with cells B and D as illustrated with x in Fig.
This is because the objective of the analyses is just to show how fieldof-view, image resolution, and depth-of-field parameters as well as pan/tilt/zoom control speed constrain the cell radius R. Once a radius R satisfying all constraints is obtained, the detailed 3D parameter design can be done easily by simulation.