Abstract: The present disclosure provides methods, apparatuses, and computer-readable mediums for performing multi-frame de-fencing by a device. In some embodiments, a method includes obtaining an image burst having at least one portion of a background scene obstructed by an opaque obstruction. The method further includes generating a plurality of obstruction masks marking the at least one portion of the background scene obstructed by the opaque obstruction in images of the image burst. The method further includes computing a motion of the background scene, with respect to a keyframe selected from the plurality of images, by applying an occlusion-aware optical flow model. The method further includes reconstructing the selected keyframe by providing a combination of features to an image fusion and inpainting network. The method further includes providing, to the user, the reconstructed keyframe comprising an unobstructed version of the background scene of the image burst.

Abstract: 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.

Abstract: 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.