From a single image, humans are able to perceive the full 3D shape of an object by exploiting learned shape priors from everyday life. Contemporary single-image 3D reconstruction algorithms aim to solve this task in a similar fashion, but often end up with priors that are highly biased by training classes.
The RePaint system reproduces paintings by combining two approaches called color-contoning and half-toning, as well as a deep learning model focused on determining how to stack 10 different inks to recreate the specific shades of color.
We present a novel computational framework for high-resolution topology optimization that delivers leaps in simulation capabilities, by two orders of magnitude, from the state-of-the-art approaches.
