公开(公告)号：US20200118253A1

公开(公告)日：2020-04-16

申请号：US16188479

申请日：2018-11-13

Applicant: Adobe Inc.

Inventor： Jonathan Eisenmann ,  Zhe Lin ,  Matthew Fisher

IPC: G06T5/00 ,  G06T5/50

Abstract: In some embodiments, an image manipulation application receives a two-dimensional background image and projects the background image onto a sphere to generate a sphere image. Based on the sphere image, an unfilled environment map containing a hole area lacking image content can be generated. A portion of the unfilled environment map can be projected to an unfilled projection image using a map projection. The unfilled projection image contains the hole area. A hole filling model is applied to the unfilled projection image to generate a filled projection image containing image content for the hole area. A filled environment map can be generated by applying an inverse projection of the map projection on the filled projection image and by combining the unfilled environment map with the generated image content for the hole area of the environment map.

公开(公告)号：US20200074682A1

公开(公告)日：2020-03-05

申请号：US16675641

申请日：2019-11-06

Applicant: ADOBE INC.

Inventor： Kalyan K. Sunkavalli ,  Yannick Hold-Geoffroy ,  Sunil Hadap ,  Matthew David Fisher ,  Jonathan Eisenmann ,  Emiliano Gambaretto

IPC: G06T7/80 ,  G06N3/08 ,  G06T7/00 ,  G06N3/04

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to generating training image data for a convolutional neural network, encoding parameters into a convolutional neural network, and employing a convolutional neural network that estimates camera calibration parameters of a camera responsible for capturing a given digital image. A plurality of different digital images can be extracted from a single panoramic image given a range of camera calibration parameters that correspond to a determined range of plausible camera calibration parameters. With each digital image in the plurality of extracted different digital images having a corresponding set of known camera calibration parameters, the digital images can be provided to the convolutional neural network to establish high-confidence correlations between detectable characteristics of a digital image and its corresponding set of camera calibration parameters. Once trained, the convolutional neural network can receive a new digital image, and based on detected image characteristics thereof, estimate a corresponding set of camera calibration parameters with a calculated level of confidence.

公开(公告)号：US10515460B2

公开(公告)日：2019-12-24

申请号：US15826331

申请日：2017-11-29

Applicant: ADOBE INC.

Inventor： Kalyan K. Sunkavalli ,  Yannick Hold-Geoffroy ,  Sunil Hadap ,  Matthew David Fisher ,  Jonathan Eisenmann ,  Emiliano Gambaretto

IPC: G06T7/80 ,  G06N3/08 ,  G06T7/00

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to generating training image data for a convolutional neural network, encoding parameters into a convolutional neural network, and employing a convolutional neural network that estimates camera calibration parameters of a camera responsible for capturing a given digital image. A plurality of different digital images can be extracted from a single panoramic image given a range of camera calibration parameters that correspond to a determined range of plausible camera calibration parameters. With each digital image in the plurality of extracted different digital images having a corresponding set of known camera calibration parameters, the digital images can be provided to the convolutional neural network to establish high-confidence correlations between detectable characteristics of a digital image and its corresponding set of camera calibration parameters. Once trained, the convolutional neural network can receive a new digital image, and based on detected image characteristics thereof, estimate a corresponding set of camera calibration parameters with a calculated level of confidence.

公开(公告)号：US10475169B2

公开(公告)日：2019-11-12

申请号：US15824943

申请日：2017-11-28

Applicant: Adobe Inc.

Inventor： Kalyan Sunkavalli ,  Mehmet Ersin Yumer ,  Marc-Andre Gardner ,  Xiaohui Shen ,  Jonathan Eisenmann ,  Emiliano Gambaretto

IPC: G06T5/00 ,  G06T9/00 ,  G06T7/90 ,  G06T1/00

Abstract: Systems and techniques for estimating illumination from a single image are provided. An example system may include a neural network. The neural network may include an encoder that is configured to encode an input image into an intermediate representation. The neural network may also include an intensity decoder that is configured to decode the intermediate representation into an output light intensity map. An example intensity decoder is generated by a multi-phase training process that includes a first phase to train a light mask decoder using a set of low dynamic range images and a second phase to adjust parameters of the light mask decoder using a set of high dynamic range image to generate the intensity decoder.