Both techniques advance microscopy by relying upon deep learning — using data to “train ... In thousands of training runs, the neural network learned how to take a 2D image and infer accurate 3D ...

a single Person image is changed into 3D from 2D using PyTorch3D - #D Deep Learning PIFu - Pixel-aligned Implicit Function This proposes an end-to-end deep learning method for digitizing highly ...

Researchers have developed a novel deep-learning method that simplifies the creation of holograms, allowing 3D images to be generated directly from 2D photos captured with standard cameras. This ...

However, obtaining a substantial number of ground-truth images for training poses significant challenges in real-world scenarios like phase retrieval or 2D/3D imaging applications ... (FR), in ...

Nowadays, images have been superseded by point clouds, which provide 3D information, thereby expediting and enhancing the estimated motion. In this paper, we dig deeply into scene flow estimation in ...

The reduced dimensionality and heterostructures of 2D materials make them promising candidates for the fabrication of photonic and optical devices. The electrical, mechanical, and optical properties ...

propose a novel approach based on deep learning that further streamlines hologram generation by producing 3D images directly from regular 2D color images captured using ordinary cameras. of holograms, ...

Here, we use a deep learning network to infer the pose of point cloud data and 3D structure. Our algorithm HOLLy (Hypothesised Object from Light Localisations) allows us to perform a completely ...