3D reconstruction from multiple images
creation of a 3D model from a set of images
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 to project 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 that the 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 years, deep learning-based approaches have significantly advanced the field of multi-view 3D reconstruction. Most notably Neural Radiance Fields (NeRF) utilize fully-connected neural networks to optimize continuous volumetric scene functions from a sparse set of input views, enabling high-quality photorealistic novel view synthesis without explicity creating traditional geometric meshes.
The public source identifies “3D reconstruction from multiple images” as creation of a 3D model from a set of images. This brief keeps that definition visible, then builds a research path around reconstruction, multiple and images.
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This entry incorporates text from “3D reconstruction from multiple images” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.