公开(公告)号：US11367206B2

公开(公告)日：2022-06-21

申请号：US16790056

申请日：2020-02-13

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Oliver Wang ,  Mai Long ,  Ke Xian ,  Jianming Zhang

IPC: G06T7/00 ,  G06T7/50 ,  G06T7/13 ,  G06N3/08 ,  G06N3/04

Abstract: In order to provide monocular depth prediction, a trained neural network may be used. To train the neural network, edge detection on a digital image may be performed to determine at least one edge of the digital image, and then a first point and a second point of the digital image may be sampled, based on the at least one edge. A relative depth between the first point and the second point may be predicted, and the neural network may be trained to perform monocular depth prediction using a loss function that compares the predicted relative depth with a ground truth relative depth between the first point and the second point.

公开(公告)号：US20210256717A1

公开(公告)日：2021-08-19

申请号：US16790056

申请日：2020-02-13

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Oliver Wang ,  Mai Long ,  Ke Xian ,  Jianming Zhang

IPC: G06T7/50 ,  G06T7/13 ,  G06N3/08

Abstract: In order to provide monocular depth prediction, a trained neural network may be used. To train the neural network, edge detection on a digital image may be performed to determine at least one edge of the digital image, and then a first point and a second point of the digital image may be sampled, based on the at least one edge. A relative depth between the first point and the second point may be predicted, and the neural network may be trained to perform monocular depth prediction using a loss function that compares the predicted relative depth with a ground truth relative depth between the first point and the second point.