Aperture Supervision for Single-View Depth Prediction

    公开(公告)号:US20210183089A1

    公开(公告)日:2021-06-17

    申请号:US16759808

    申请日:2017-11-03

    Applicant: Google LLC

    Abstract: Example embodiments allow for training of artificial neural networks (ANNs) to generate depth maps based on images. The ANNs are trained based on a plurality of sets of images, where each set of images represents a single scene and the images in such a set of images differ with respect to image aperture and/or focal distance. An untrained ANN generates a depth map based on one or more images in a set of images. This depth map is used to generate, using the image(s) in the set, a predicted image that corresponds, with respect to image aperture and/or focal distance, to one of the images in the set. Differences between the predicted image and the corresponding image are used to update the ANN. ANNs tramed in this manner are especially suited for generating depth maps used to perform simulated image blur on small-aperture images.)

    Aperture supervision for single-view depth prediction

    公开(公告)号:US11113832B2

    公开(公告)日:2021-09-07

    申请号:US16759808

    申请日:2017-11-03

    Applicant: Google LLC

    Abstract: Example embodiments allow for training of artificial neural networks (ANNs) to generate depth maps based on images. The ANNs are trained based on a plurality of sets of images, where each set of images represents a single scene and the images in such a set of images differ with respect to image aperture and/or focal distance. An untrained ANN generates a depth map based on one or more images in a set of images. This depth map is used to generate, using the image(s) in the set, a predicted image that corresponds, with respect to image aperture and/or focal distance, to one of the images in the set. Differences between the predicted image and the corresponding image are used to update the ANN. ANNs tramed in this manner are especially suited for generating depth maps used to perform simulated image blur on small-aperture images.

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