Image-based modeling and rendering explained

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.

Light modeling

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

(x,y,z) , its orientation (\theta,\phi) , its wavelength ( λ ) and its time (t) : P (x,y,z,\theta,\phi, λ ,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

• 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

See also

• View synthesis
• 3D reconstruction
• Structure from motion

External links

• Quan, Long. Image-based modeling. Springer Science & Business Media, 2010. https://www.springer.com/us/book/9781441966780
• Ce Zhu . Shuai Li . Depth Image Based View Synthesis: New Insights and Perspectives on Hole Generation and Filling. IEEE Transactions on Broadcasting. 62. 82–93. 2016. 10.1109/TBC.2015.2475697. 1. 19100077 .
• Mansi Sharma . Santanu Chaudhury . Brejesh Lall . M.S. Venkatesh . A flexible architecture for multi-view 3DTV based on uncalibrated cameras. Journal of Visual Communication and Image Representation. 25. 599–621. 2014. 10.1016/j.jvcir.2013.07.012. 4.
• Mansi Sharma . Santanu Chaudhury . Brejesh Lall . In 22nd International Conference on Pattern Recognition (ICPR), Stockholm, 2014 . Kinect-Variety Fusion: A Novel Hybrid Approach for Artifacts-Free 3DTV Content Generation. 2014. 10.1109/ICPR.2014.395.
• Mansi Sharma . Santanu Chaudhury . Brejesh Lall . Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing, ACM New York, NY, USA. 3DTV view generation with virtual pan/tilt/zoom functionality. 2012. 10.1145/2425333.2425374.