Facebook Technologies, LLC

Meta Platforms Technologies, LLC

Todd Goodall

Anjul Patney

Anton S Kaplanyan

Jamorn Sriwasansak

Romain Bachy

Thomas Sebastian Leimkuhler

Trisha Lian

Gizem Rufo

In one embodiment, a method includes determining characteristics of one or more areas in an image by analyzing pixels in the image, computing a sampling density for each of the one or more areas in the image based on the characteristics of the one or more areas, generating samples corresponding to the image by sampling pixels in each of the one or more areas according to the associated sampling density, and providing the samples to a machine-learning model as an input, where the machine-learning model is configured to reconstruct the image by processing the samples.

Adaptive sampling of images

In one embodiment, a method includes accessing a pair of stereo images for a scene, where each image of the pair of stereo images has incomplete pixel information and k channels, stacking the pair of stereo images to form a stacked input image with 2k channels, processing the stacked input image using a machine-learning model to generate a stacked output image with 2k channels, and separating the stacked output image with 2k channels into a pair of reconstructed stereo images for the scene, where each image of the pair of reconstructed stereo images has complete pixel information and k channels.

Processing stereo images with a machine-learning model

In one embodiment, a computing system may access a video including a first frame and a second frame. The computing system may determine first sampling locations for the first frame and determine second sampling locations for the second frame by transforming the first sampling locations to the second frame according to an optical flow between the first frame and the second frame. The computing system may select a subset of the second sampling locations based on a comparison between pixels in the first frame corresponding to the first sampling locations and pixels in the second frame corresponding to the second sampling locations. The computing system may define one or more rejection areas in the second frame based on the subset of the second sampling locations to determine third sampling locations in areas outside of the rejection areas. The computing system may generate a sample of the video.

Efficient motion-compensated spatiotemporal sampling

In one embodiment, a computing system may receive a video including a sequence of frames. The computing system may access a three-dimensional mask that specifies pixel-sampling locations, the three-dimensional mask having a first dimension and a second dimension corresponding to a spatial domain and a third dimension corresponding to a temporal domain. Blue noise property may be present in the pixel-sampling locations that are associated with each of a plurality of two-dimensional spatial slices of the three-dimensional mask in the spatial domain and the pixel-sampling locations that are associated with each of a plurality of one-dimensional temporal slices of the three-dimensional mask in the temporal domain. The computing system may generate a sample of the video by sampling the sequence of frames using the three-dimensional mask.

Blue noise mask for video sampling

In one embodiment, a method includes projecting a source image onto a surface using a lens approximation component, where the surface is associated with sampling points approximating photoreceptors of an eye, where each sampling point has a corresponding photoreceptor type, sampling color information from the projected source image at the sampling points, where the color information sampled at each sampling point depends on the corresponding photoreceptor type, accessing pooling units approximating retinal ganglion cells (RGCs) of the eye, where each pooling unit is associated with groups of one or more of the sampling points, calculating weighted aggregations of the sampled color information associated with the groups of one or more sampling points associated with each pooling unit, and computing a perception profile for the source image based on the weighted aggregations associated with each of the pooling units.

Growing community of inventors

Mill Valley, CA, United States of America

Todd Goodall