Samsung Electronics Co., Ltd.

Stavros Tsogkas

Allan Jepson

Aleksai Levinshtein

Tristan Ty Aumentado-armstrong

Xinyu Sun

Haicheng Wang

Sven Josef Dickinson

Vineeth Subrahmanya Bhaskara

The present disclosure provides methods, apparatuses, and computer-readable mediums for representing shapes with probabilistic directed distance fields. In some embodiments, a method includes obtaining a camera representation and a latent shape vector representation of a scene. The camera representation indicates position information and direction information of a view of the scene. The method further includes calculating, based on the latent shape vector representation of the scene, a visibility score and a depth for each ray of a plurality of rays emanating from a corresponding plurality of positions and directions. The plurality of positions and directions are determined from the camera representation of the scene. The method further includes generating renders of geometric information of the scene using the visibility score and the depth of the plurality of rays.

Representing 3D shapes with probabilistic directed distance fields

A method for training a super-resolution network may include obtaining a low resolution image; generating, using a first machine learning model, a first high resolution image based on the low resolution image; generating, using a second machine learning model, a second high resolution image based on the first high resolution image and an unpaired dataset of high resolution images; obtaining a training data set using the low resolution image and the second high resolution image; and training the super-resolution network using the training data set.

Unsupervised super-resolution training data construction

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Toronto, Canada

Stavros Tsogkas