Epstein Files Full PDF

This article includes a list of references, related reading, or external links, but its sources remain unclear because it lacks inline citations. Please help improve this article by introducing more precise citations. (April 2019) (Learn how and when to remove this message)

In computer graphics and computer vision, image-based modeling and rendering (IBMR) methods rely on a set of two-dimensional images of a scene to generate a three-dimensional model and then render 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.

Instead of considering only the physical model of a solid, IBMR methods usually focus more on light modeling. The fundamental concept behind IBMR is the plenoptic illumination function which is a parametrisation of the light 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 ${\displaystyle (x,y,z)}$, its orientation ${\displaystyle (\theta ,\phi )}$, its wavelength ${\displaystyle (\lambda )}$ and its time ${\displaystyle (t)}$: ${\displaystyle P(x,y,z,\theta ,\phi ,\lambda ,t)}$. 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).

• View morphing 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 a photo-consistency check

• View synthesis
• 3D reconstruction
• Structure from motion

• 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.