公开(公告)号：US12165380B2

公开(公告)日：2024-12-10

申请号：US17309206

申请日：2019-11-15

Applicant: GOOGLE LLC

Inventor： Chloe LeGendre ,  Wan-Chun Ma ,  Graham Fyffe ,  John Flynn ,  Jessica Busch ,  Paul Debevec

IPC: G06V10/60 ,  G06V10/44 ,  G06V10/75 ,  G06V20/40

Abstract: An example method, apparatus, and computer-readable storage medium are provided to predict high-dynamic range (HDR) lighting from low-dynamic range (LDR) background images. In an example implementation, a method may include receiving low-dynamic range (LDR) background images of scenes, each LDR background image captured with appearance of one or more reference objects with different reflectance properties; and training a lighting estimation model based at least on the received LDR background images to predict high-dynamic range (HDR) lighting based at least on the trained model. In another example implementation, a method may include capturing a low-dynamic range (LDR) background image of a scene from an LDR video captured by a camera of the electronic computing device; predicting high-dynamic range (HDR) lighting for the image, the predicting, using a trained model, based at least on the LDR background image; and rendering a virtual object based at least on the predicted HDR lighting.

公开(公告)号：US20210406581A1

公开(公告)日：2021-12-30

申请号：US17309206

申请日：2019-11-15

Applicant: GOOGLE LLC

Inventor： Chloe LeGendre ,  Wan-Chun Ma ,  Graham Fyffe ,  John Flynn ,  Jessica Busch ,  Paul Debevec

IPC: G06K9/46 ,  G06K9/00 ,  G06K9/62

Abstract: An example method, apparatus, and computer-readable storage medium are provided to predict high-dynamic range (HDR) lighting from low-dynamic range (LDR) background images. In an example implementation, a method may include receiving low-dynamic range (LDR) background images of scenes, each LDR background image captured with appearance of one or more reference objects with different reflectance properties; and training a lighting estimation model based at least on the received LDR background images to predict high-dynamic range (HDR) lighting based at least on the trained model. In another example implementation, a method may include capturing a low-dynamic range (LDR) background image of a scene from an LDR video captured by a camera of the electronic computing device; predicting high-dynamic range (HDR) lighting for the image, the predicting, using a trained model, based at least on the LDR background image; and rendering a virtual object based at least on the predicted HDR lighting.