By Takashi Matsuyama, Shohei Nobuhara, Takeshi Takai, Tony Tung
This ebook provides a large evaluate of state of the art 3D video creation applied sciences and purposes. The textual content opens with a concise advent to the sector, ahead of interpreting the layout and calibration tools for multi-view digicam platforms, together with useful implementation applied sciences. various algorithms are then defined for generating 3D video from video information. a range of 3D video functions also are established. positive aspects: describes real-time synchronized multi-view video catch, and item monitoring with a bunch of lively cameras; discusses geometric and photometric digital camera calibration, and 3D video studio layout with lively cameras; examines 3D form and movement reconstruction, texture mapping and photo rendering, and lighting fixtures surroundings estimation; demonstrates beautiful 3D visualization, visible contents research and enhancing, 3D physique motion research, and knowledge compression; highlights the rest demanding situations and the fascinating avenues for destiny examine in 3D video technology.
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Additional resources for 3D Video and Its Applications
2(b)). That is, the cameras do not observe the object but the cells where the object moves. Step 3: Camera calibration With the pan/tilt/zoom parameters for each cell, conduct the static camera calibration using a reference pattern located in each cell (cf. Fig. 2(c)). Step 4: Object tracking While tracking and capturing multi-view video of an object, some cameras observe the object in cell A with the pan/tilt/zoom parameters predetermined for cell A, and others observe its neighboring cell(s) B with the pan/tilt/zoom parameters predetermined for cell B.
Intrinsic and lens distortion parameters estimation by Zhang . Extrinsic parameter calibration by 8-point algorithm . Non-linear optimization (bundle adjustment). Global scale and position adjustment. 1 Intrinsic and Lens Distortion Parameters Estimation The most standard camera calibration method is a planar pattern-based method proposed by Zhang [3, 4, 33]. Given a set of planar reference 3D points whose positions on the plane are known, it estimates the camera position and posture with respect to the reference, and the intrinsic and lens distortion parameters.
Then, the origin of the camera C coordinate system is represented by λBC ARB BTC + λAB ATB . 3 Camera Calibration 33 Fig. 7 Extrinsic parameter estimation. With several known 2D-to-2D point correspondences in a pair of observed images (p1 to p1 , . . , pn to pn ), the relative 3D position and posture of two cameras (R and T ) can be estimated up to scale Fig. 8 2D-to-2D point correspondences by using chess corners (left, by ) and sphere centers (right) 7. By equating the above two representations of the origin of the camera C coordinate system, we can obtain the constraint for three scale factors.
3D Video and Its Applications by Takashi Matsuyama, Shohei Nobuhara, Takeshi Takai, Tony Tung