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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.
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.
3D reconstruction/pose estimation 2020 [187] B. Biggs et al. The Oxford-IIIT Pet Dataset 37 categories of pets with roughly 200 images of each. Breed labeled, tight bounding box, foreground-background segmentation. ~ 7,400 Images, text Classification, object detection 2012 [186] [188] O. Parkhi et al. Corel Image Features Data Set
The analysis-by-synthesis approach enabled the mapping of the 3D and 2D domains and a new representation of 3D shape and appearance. Their work is the first to introduce a statistical model for faces that enabled 3D reconstruction from 2D images and a parametric face space for controlled manipulation. [2]
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]
This method uses X-ray images for 3D Reconstruction and to develop 3D models with low dose radiations in weight bearing positions. In NSCC algorithm, the preliminary step is calculation of an initial solution. Firstly anatomical regions from the generic object are defined. Secondly, manual 2D contours identification on the radiographs is performed.
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]