Princeton University

King Abdullah University of Science and Technology

Seung-Hwan Baek

Qiang Fu

Wolfgang Heidrich

Felix Heide

Ilya Chugunov

The present disclosure relates generally to image processing, and more particularly, toward techniques for structured illumination and reconstruction of three-dimensional (3D) images. Disclosed herein is a method to jointly learn structured illumination and reconstruction, parameterized by a diffractive optical element and a neural network in an end-to-end fashion. The disclosed approach has a differentiable image formation model for active stereo, relying on both wave and geometric optics, and a trinocular reconstruction network. The jointly optimized pattern, dubbed 'Polka Lines,' together with the reconstruction network, makes accurate active-stereo depth estimates across imaging conditions. The disclosed method is validated in simulation and used with an experimental prototype, and several variants of the Polka Lines patterns specialized to the illumination conditions are demonstrated.

Polka lines: learning structured illumination and reconstruction for active stereo

The microlens amplitude masks for flying pixel removal in time-of-flight imaging includes systems, devices, methods, and instructions for image depth determination, including receiving an image, adding noise to the image, determining a set of correlation images, each correlation image having a varying phase offset, for each pixel of the image, generating a masked pixel by applying a mask array, and for each masked pixel, determining the depth of the masked pixel to generate a depth map for the image on a per pixel basis.

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Princeton, NJ, United States of America

Seung-Hwan Baek