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Machine Learning Based Solutions Machine learning enables learning the correspondance between the subtle features in the input and the respective 3D equivalent. Deep neural networks have shown to be highly effective for 3D reconstruction from a single color image. [15] This works even for non-photorealistic input images such as sketches.
A neural radiance field (NeRF) is a method based on deep learning for reconstructing a three-dimensional representation of a scene from two-dimensional images. The NeRF model enables downstream applications of novel view synthesis, scene geometry reconstruction, and obtaining the reflectance properties of the scene.
One group of deep learning reconstruction algorithms apply post-processing neural networks to achieve image-to-image reconstruction, where input images are reconstructed by conventional reconstruction methods. Artifact reduction using the U-Net in limited angle tomography is such an example application. [6]
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.
In learned iterative reconstruction, the updating algorithm is learned from training data using techniques from machine learning such as convolutional neural networks, while still incorporating the image formation model. This typically gives faster and higher quality reconstructions and has been applied to CT [4] and MRI reconstruction. [5]
x (3D point) is the homogeneous representation of the resulting 3D point. The ∼ {\displaystyle \sim \,} sign implies that τ {\displaystyle \tau \,} is only required to produce a vector which is equal to x up to a multiplication by a non-zero scalar since homogeneous vectors are involved.
The prior knowledge is statistically extracted from a database of 3D examples and used as a basis to represent or generate new plausible objects of that class. Its effectiveness lies in the ability to efficiently encode this prior information, enabling the solution of otherwise ill-posed problems (such as single-view 3D object reconstruction). [2]
Making a 3D-model of a Viking belt buckle using a hand held VIUscan 3D laser scanner. 3D scanning is the process of analyzing a real-world object or environment to collect three dimensional data of its shape and possibly its appearance (e.g. color). The collected data can then be used to construct digital 3D models.