A neural field network can create a continuous 3D model from a limited number of 2D images, and it does it without being trained on other samples.

Researchers at NVIDIA have come up with a clever machine learning technique for taking 2D images and fleshing them out into 3D models.

Machine learning for morphable materials New platform can program the transformation of 2D stretchable surfaces into specific 3D shapes Date: January 12, 2022 Source: Harvard John A. Paulson ...

New artificial intelligence techniques can spot patterns not only in 2D images but on spheres and other curved surfaces, lifting AI out of “flatland.” ...

Nvidia Research and others collaborated to create the DIB-R framework that can predict 3D properties from 2D images to create 3D models.

(Image sourced via axial3D ) A Northern Ireland healthcare SME is using a wide swathe of Amazon Web Services (AWS) technologies to enable its proprietary algorithms to convert CT and MRI scans into ...

Researchers from the McKelvey School of Engineering at Washington University in St. Louis have developed a machine learning algorithm that can create a continuous 3D model of cells from a partial set ...

A neural field network developed at Washington University in St. Louis, can create a continuous 3D model from a limited number of 2D images, and it does it without being trained on other samples.