3D reconstruction from multiple images information
Creation of a 3D model from a set of images
This article's factual accuracy may be compromised due to out-of-date information. Please help update this article to reflect recent events or newly available information.(October 2019)
A 3D selfie in 1:20 scale printed by Shapeways using gypsum-based printing, created by Madurodam miniature park from 2D pictures taken at its Fantasitron photo booth3D models are generated from 2D pictures taken at the Fantasitron 3D photo booth at Madurodam.Generating and reconstructing 3D shapes from single or multi-view depth maps or silhouettes[1]
3D reconstruction from multiple images is the creation of three-dimensional models from a set of images. It is the reverse process of obtaining 2D images from 3D scenes.
The essence of an image is a projection from a 3D scene onto a 2D plane, during which process the depth is lost. The 3D point corresponding to a specific image point is constrained to be on the line of sight. From a single image, it is impossible to determine which point on this line corresponds to the image point. If two images are available, then the position of a 3D point can be found as the intersection of the two projection rays. This process is referred to as triangulation. The key for this process is the relations between multiple views which convey the information that corresponding sets of points must contain some structure and that this structure is related to the poses and the calibration of the camera.
In recent decades, there is an important demand for 3D content for computer graphics, virtual reality and communication, triggering a change in emphasis for the requirements. Many existing systems for constructing 3D models are built around specialized hardware (e.g. stereo rigs) resulting in a high cost, which cannot satisfy the requirement of its new applications. This gap stimulates the use of digital imaging facilities (like a camera). An early method was proposed by Tomasi and Kanade.[2] They used an affine factorization approach to extract 3D from images sequences. However, the assumption of orthographic projection is a significant limitation of this system.
^"Soltani, A. A., Huang, H., Wu, J., Kulkarni, T. D., & Tenenbaum, J. B. Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes With Deep Generative Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1511-1519)". GitHub. 6 March 2020.
^C. Tomasi and T. Kanade, “Shape and motion from image streams under orthography: A factorization approach”, International Journal of Computer Vision, 9(2):137-154, 1992.
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