US 11,941,830 B2
Depth sensing using temporal coding
Niv Kantor, Redwood City, CA (US); Ricardo Garcia, San Bruno, CA (US); Nadav Grossinger, Hillsborough, CA (US); Robert Hasbun, San Jose, CA (US); and Nitay Romano, Nitzan (IL)
Assigned to Meta Platforms Technologies, LLC, Menlo Park, CA (US)
Filed by META PLATFORMS TECHNOLOGIES, LLC, Menlo Park, CA (US)
Filed on Oct. 7, 2021, as Appl. No. 17/496,021.
Application 17/496,021 is a continuation of application No. 16/150,204, filed on Oct. 2, 2018, granted, now 11,158,074, issued on Oct. 26, 2021.
Prior Publication US 2022/0028099 A1, Jan. 27, 2022
Int. Cl. G06T 7/521 (2017.01); G01S 17/48 (2006.01)
CPC G06T 7/521 (2017.01) [G01S 17/48 (2013.01); G06T 2207/10028 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising, by a computing device:
capturing, by a camera of the computing device, and during a time period, images comprising a plurality of detected patterns corresponding to reflections of a plurality of distinct projected patterns projected by a projector, wherein an intensity of each of the plurality of distinct projected patterns varies over the time period in accordance with a predetermined temporal lighting-characteristic pattern associated with the projected pattern, wherein a rate at which the images are captured by the camera is synchronized with a rate at which the intensity varies, and wherein the camera is configured to capture consecutive images over an interval of time corresponding to an interval of time between changes in the intensity of each of the plurality of distinct projected patterns;
determining, based on the images, a detected temporal lighting-characteristic pattern for each of the detected patterns;
detecting a correspondence between a first detected pattern of the detected patterns and a first projected pattern of the projected patterns by comparing the detected temporal lighting-characteristic pattern of the first detected pattern to the predetermined temporal lighting-characteristic pattern of the first projected pattern; and
computing a depth associated with the first detected pattern based on the correspondence.