Simulating depth of field

Patent metadata

Field	Value
Publication number	US-10484599-B2
Application number	US-201715448425-A
Country	US
Kind code	B2
Filing date	Mar 2, 2017
Priority date	Oct 25, 2016
Publication date	Nov 19, 2019
Grant date	Nov 19, 2019

Abstract

The present disclosure provides approaches to simulating depth of field. In some implementations, an optimal scan distance of a camera from a subject in a physical environment is determined for a scan of the subject by the camera. A blur level is iteratively updated to correspond to a proximity of the camera to the determined optimal scan distance as the proximity changes during the scan. For each update to the blur level from the iteratively updating, an image comprising a three-dimensional (3D) model of the physical environment depicted at the updated blur level is generated on a user device associated with the scan.

First claim

What is claimed is:

1. A computer-implemented method comprising:
determining an optimal scan distance of a camera from a subject in a physical environment for a scan of the subject by the camera;
iteratively updating a blur level to correspond to a proximity of the camera to the determined optimal scan distance as the proximity changes during the scan; and
for each update to the blur level from the iteratively updating, generating, on a user device associated with the scan, an image comprising a three-dimensional (3D) model of the physical environment depicted at the updated blur level, the 3D model produced from the scan, the generating comprising:
blending a first texture with a second texture based on determining that a value of the blur level is greater than a first value corresponding to the first blur texture and a second value corresponding to the second blur texture, and
performing the blurring of the 3D model with the blended first texture and second texture for the image.

2. The computer-implemented method of claim 1 , wherein the iteratively updating comprises in each iteration determining the proximity, wherein the updated blur level is proportional to the determined proximity.

3. The computer-implemented method of claim 1 , wherein the determining of the optimal scan distance is based on tracking a position of the camera in the physical environment with sensors of the user device.

4. The computer-implemented method of claim 1 , wherein in the iteratively updating, the blur level increases based on the proximity decreasing and decreases based on the proximity increasing.

5. The computer-implemented method of claim 1 , wherein the generating of the image comprises a particle shader blurring on a particle of the 3D model at the blur level.

6. The computer-implemented method of claim 1 , wherein the iteratively updating comprises for each iteration, determining the updated blur level based on a position of the camera in the physical environment.

7. The computer-implemented method of claim 1 , determining the blur level such that the blur level is minimized when the camera is at the optimal scan distance.

8. One or more computer-storage media having executable instructions embodied thereon, which, when executed by one or more processors, cause the one or more processors to perform a method comprising:
determining an optimal scan distance of a camera from a subject in a physical environment for a scan of the subject by the camera;
iteratively updating a blur level to correspond to a proximity of the camera to the determined optimal scan distance as the proximity changes during the scan; and
for each update to the blur level from the iteratively updating, generating, on a user device associated with the scan, an image comprising a three-dimensional (3D) model of the physical environment depicted at the updated blur level, the 3D model produced from the scan, the generating comprising:
blending a first texture with a second texture based on determining that a value of the blur level is greater than a first value corresponding to the first blur texture and a second value corresponding to the second blur texture, and
performing the blurring of the 3D model with the blended first texture and second texture for the image.

9. The one or more computer-storage media of claim 8 , wherein the iteratively updating comprises in each iteration determining the proximity, wherein the updated blur level is proportional to the determined proximity.

10. The one or more computer-storage media of claim 8 , wherein the determining of the optimal scan distance is based on tracking a position of the camera in the physical environment with sensors of the user device.

11. The one or more computer-storage media of claim 8 , wherein in the iteratively updating, the blur level increases based on the proximity decreasing and decreases based on the proximity increasing.

12. The one or more computer-storage media of claim 8 , wherein the generating of the image comprises a particle shader blurring on a particle of the 3D model at the blur level.

13. The one or more computer-storage media of claim 8 , wherein the iteratively updating comprises for each iteration, determining the updated blur level based on a position of the camera in the physical environment.

14. The one or more computer-storage media of claim 8 , the method further comprising determining the blur level such that the blur level is minimized when the camera is at the optimal scan distance.

15. A system, comprising:
one or more processors;
one or more computer-storage media having executable instructions embodied thereon, which, when executed by one or more processors, cause the one or more processors to perform a method comprising:
determining an optimal scan distance of a camera from a subject in a physical environment for a scan of the subject by the camera;
iteratively updating a blur level to correspond to a proximity of the camera to the determined optimal scan distance as the proximity changes during the scan; and
for each update to the blur level from the iteratively updating, generating, on a user device associated with the scan, an image comprising a three-dimensional (3D) model of the physical environment depicted at the updated blur level, the 3D model produced from the scan, the generating comprising:
blending a first texture with a second texture based on determining that a value of the blur level is greater than a first value corresponding to the first blur texture and a second value corresponding to the second blur texture, and
performing the blurring of the 3D model with the blended first texture and second texture for the image.

16. The system of claim 15 , wherein the iteratively updating comprises in each iteration determining the proximity, wherein the updated blur level is proportional to the determined proximity.

17. The system of claim 15 , wherein the determining of the optimal scan distance is based on tracking a position of the camera in the physical environment with sensors of the user device.

18. The system of claim 15 , wherein in the iteratively updating, the blur level increases based on the proximity decreasing and decreases based on the proximity increasing.

19. The system of claim 15 , wherein the generating of the image comprises a particle shader blurring on a particle of the 3D model at the blur level.

20. The system of claim 15 , wherein the iteratively updating comprises for each iteration, determining the updated blur level based on a position of the camera in the physical environment.

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Ryan Patrick

Classifications

• H04N23/959

  by adjusting depth of field during image capture, e.g. maximising or setting range based on scene characteristics · CPC title

• H04N23/63

  by using electronic viewfinders · CPC title

• H04N23/64 Primary

  Computer-aided capture of images, e.g. transfer from script file into camera, check of taken image quality, advice or proposal for image composition or decision on when to take image · CPC title

• G06T15/04 Primary

  Texture mapping · CPC title

• H04N13/271

  wherein the generated image signals comprise depth maps or disparity maps · CPC title