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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.
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]
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 booth 3D 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 ...
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.
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]
Point set registration is the process of aligning two point sets. Here, the blue fish is being registered to the red fish. In computer vision, pattern recognition, and robotics, point-set registration, also known as point-cloud registration or scan matching, is the process of finding a spatial transformation (e.g., scaling, rotation and translation) that aligns two point clouds.
CHIRP (Continuous High-resolution Image Reconstruction using Patch priors) is a Bayesian algorithm used to perform a deconvolution on images created in radio astronomy. The acronym was coined by lead author Katherine L. Bouman in 2016.
The 3D geometry and textures are captured onto a 3D model by a 3D reconstruction method, such as sampling the target by means of 3D scanning with an RGB XYZ scanner such as Arius3d or Cyberware (textures from photos, not pure RGB XYZ scanner), stereophotogrammetrically from synchronized photos or even from enough repeated non-simultaneous photos.