US 12,260,571 B2
Depth map completion in visual content using semantic and three-dimensional information
Hong Cai, San Diego, CA (US); Shichong Peng, Seattle, WA (US); Janarbek Matai, San Diego, CA (US); Jamie Menjay Lin, San Diego, CA (US); Debasmit Das, San Diego, CA (US); and Fatih Murat Porikli, San Diego, CA (US)
Assigned to QUALCOMM Incorporated, San Diego, CA (US)
Filed by QUALCOMM Incorporated, San Diego, CA (US)
Filed on Feb. 4, 2022, as Appl. No. 17/650,027.
Prior Publication US 2023/0252658 A1, Aug. 10, 2023
Int. Cl. G06K 9/00 (2022.01); G06N 3/045 (2023.01); G06T 7/10 (2017.01); G06T 7/50 (2017.01)
CPC G06T 7/50 (2017.01) [G06N 3/045 (2023.01); G06T 7/10 (2017.01); G06T 2207/20084 (2013.01); G06T 2207/20212 (2013.01)] 26 Claims
OG exemplary drawing
 
1. A method, comprising:
generating, through a segmentation neural network, a segmentation map based on an image of a scene, wherein the segmentation map comprises a plurality of segments, and wherein each segment of the plurality of segments is associated with one of a plurality of categories;
generating, through a first depth neural network, a first depth map of the scene based on a depth measurement of the scene;
generating a plurality of masks based on the segmentation map and the first depth map, each mask corresponding to one of the plurality of segments;
generating a plurality of enhanced depth masks based on the plurality of masks and three-dimensional coordinate information derived from the first depth map as inputs into a depth refinement neural network, wherein each enhanced depth mask of the plurality of enhanced depth masks corresponds to one of the plurality of segments;
combining the plurality of enhanced depth maps to form a second depth map of the scene;
taking one or more actions based on the second depth map of the scene.