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Image-based modeling and rendering

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Incomputer graphics andcomputer vision,image-based modeling and rendering (IBMR) methods rely on a set of two-dimensional images of a scene togenerate a three-dimensional model and thenrender some novel views of this scene.

The traditional approach of computer graphics has been used to create a geometric model in 3D and try to reproject it onto a two-dimensional image. Computer vision, conversely, is mostly focused on detecting, grouping, and extracting features (edges, faces,etc.) present in a given picture and then trying to interpret them as three-dimensional clues. Image-based modeling and rendering allows the use of multiple two-dimensional images in order to generate directly novel two-dimensional images, skipping the manual modeling stage.

Light modeling

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Instead of considering only the physical model of a solid, IBMR methods usually focus more on light modeling. The fundamental concept behind IBMR is theplenoptic illumination function which is a parametrisation of thelight field. The plenoptic function describes the light rays contained in a given volume. It can be represented with seven dimensions: a ray is defined by its position(x,y,z){\displaystyle (x,y,z)}, its orientation(θ,ϕ){\displaystyle (\theta ,\phi )}, its wavelength(λ){\displaystyle (\lambda )} and its time(t){\displaystyle (t)}:P(x,y,z,θ,ϕ,λ,t){\displaystyle P(x,y,z,\theta ,\phi ,\lambda ,t)}. IBMR methods try to approximate the plenoptic function to render a novel set of two-dimensional images from another. Given the high dimensionality of this function, practical methods place constraints on the parameters in order to reduce this number (typically to 2 to 4).

IBMR methods and algorithms

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  • Viewmorphing generates a transition between images
  • Panoramic imaging renders panoramas using image mosaics of individual still images
  • Lumigraph relies on a dense sampling of a scene
  • Space carving generates a 3D model based on aphoto-consistency check

See also

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References

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External links

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  • Quan, Long.Image-based modeling. Springer Science & Business Media, 2010.[1]
  • Ce Zhu; Shuai Li (2016). "Depth Image Based View Synthesis: New Insights and Perspectives on Hole Generation and Filling".IEEE Transactions on Broadcasting.62 (1):82–93.doi:10.1109/TBC.2015.2475697.S2CID 19100077.
  • Mansi Sharma; Santanu Chaudhury; Brejesh Lall; M.S. Venkatesh (2014). "A flexible architecture for multi-view 3DTV based on uncalibrated cameras".Journal of Visual Communication and Image Representation.25 (4):599–621.doi:10.1016/j.jvcir.2013.07.012.
  • Mansi Sharma; Santanu Chaudhury; Brejesh Lall (2014).Kinect-Variety Fusion: A Novel Hybrid Approach for Artifacts-Free 3DTV Content Generation. In 22nd International Conference on Pattern Recognition (ICPR), Stockholm, 2014.doi:10.1109/ICPR.2014.395.
  • Mansi Sharma; Santanu Chaudhury; Brejesh Lall (2012).3DTV view generation with virtual pan/tilt/zoom functionality. Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing, ACM New York, NY, USA.doi:10.1145/2425333.2425374.
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