公开(公告)号：US20220327769A1

公开(公告)日：2022-10-13

申请号：US17639967

申请日：2020-05-04

Applicant: GOOGLE LLC

Inventor： Yun-Ta TSAI ,  Xiuming ZHANG ,  Jonathan T. BARRON ,  Sean FANELLO ,  Tiancheng SUN ,  Tianfan XUE

IPC: G06T15/50 ,  G06T15/04 ,  G06T15/20 ,  G06N3/08

Abstract: Examples relate to implementations of a neural light transport. A computing system may obtain data indicative of a plurality of UV texture maps and a geometry of an object. Each UV texture map depicts the object from a perspective of a plurality of perspectives. The computing system may train a neural network to learn a light transport function using the data. The light transport function may be a continuous function that specifies how light interacts with the object when the object is viewed from the plurality of perspectives. The computing system may generate an output UV texture map that depicts the object from a synthesized perspective based on an application of the light transport function by the trained neural network.

公开(公告)号：US20210264576A1

公开(公告)日：2021-08-26

申请号：US17260364

申请日：2019-04-01

Applicant: GOOGLE LLC

Inventor： Tiancheng SUN ,  Yun-ta TSAI ,  Jonathan BARRON

IPC: G06T5/00

Abstract: Apparatus and methods related to applying lighting models to images of objects are provided. A neural network can be trained to apply a lighting model to an input image. The training of the neural network can utilize confidence learning that is based on light predictions and prediction confidence values associated with lighting of the input image. A computing device can receive an input image of an object and data about a particular lighting model to be applied to the input image. The computing device can determine an output image of the object by using the trained neural network to apply the particular lighting model to the input image of the object.