IP Library Granted Patent US 12,488,421
Granted Patent B2
US 12,488,421 · App. 17/475,031 · Granted Dec 2, 2025

Depth estimation based on data fusion of image sensor and depth sensor frames

Inventors: Jiaoyang Yao (Singapore, SG); Fangwen Tu (Singapore, SG); Bo Li (Singapore, SG)
Assignee: Black Sesame Technologies Inc.
G06T5/50G06T2207/20182G06T2207/20221
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Quick Facts
Patent No.
US 12,488,421
App. No.
17/475,031
Granted
Dec 2, 2025
Kind
B2
Abstract

A method of depth estimation, including, receiving an image frame, determining a relative depth map based on the image frame, receiving a sparse depth frame, preprocessing the sparse depth frame, determining a scale-adjusted relative depth map based on the relative depth map and the preprocessed sparse depth frame and fusing the relative depth map and the scale-adjusted relative depth map to produce an absolute depth map.

Claims (24)

1 . A method of depth estimation, comprising:

receiving an image frame;

determining a relative depth map based on the image frame;

receiving a sparse depth frame from a sparse depth sensor;

preprocessing the sparse depth frame wherein the preprocessing includes at least one of noise reduction, hole reduction, median filtering, and occlusion reduction;

after preprocessing, extending a field of view of the sparse depth frame to the field of view of the image frame, reducing noise in the sparse depth frame, and enhancing resolution of the sparse depth frame, resulting in a sparse depth map;

determining a scale-adjusted relative depth map based on the relative depth map and the sparse depth map; and

fusing the relative depth map and the scale-adjusted relative depth map to produce an absolute depth map.

2 . The method of depth estimation of claim 1 , wherein the sparse depth frame is based on at least one of time-of-flight, light detection and ranging and structured light.

3 . The method of depth estimation of claim 1 , wherein the determining of the relative depth map is determined utilizing an encoder and decoder.

4 . The method of depth estimation of claim 1 , wherein the determining of the absolute depth map is determined utilizing an encoder and decoder.

5 . The method of depth estimation of claim 1 , wherein the fusing is performed by a fusion network.

6 . A method of depth estimation, comprising:

receiving an image frame;

determining a relative depth map based on the image frame;

receiving a sparse depth frame from a sparse depth sensor;

preprocessing the sparse depth frame wherein the preprocessing includes at least one of noise reduction, hole reduction, median filtering, and occlusion reduction;

after preprocessing, extending a field of view of the sparse depth frame to the field of view of the image frame, reducing noise in the sparse depth frame, and enhancing resolution of the sparse depth frame, resulting in a sparse depth map;

and

fusing the relative depth map and the sparse depth map to produce an absolute depth map.

7 . The method of depth estimation of claim 6 , further comprising extending a field of view of the sparse depth frame.

8 . The method of depth estimation of claim 6 , further comprising enhancing a resolution of the sparse depth frame.

9 . The method of depth estimation of claim 6 , wherein the sparse depth frame is based on at least one of time-of-flight, light detection and ranging and structured light.

10 . The method of depth estimation of claim 6 , wherein the preprocessing includes at least one of a noise reduction, a hole reduction, a median filtering and an occlusion reduction.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 26, 2021
From: YAO, JIAOYANG; TU, FANGWEN; LI, BO
To: BLACK SESAME TECHNOLOGIES INC.
Reel/Frame 057920/0120 →
Continuity (1)
Related Publication 20230083014A1 · Mar 16, 2023
References Cited (20)
US 9137512B2 · Baik et al. · 2015 [cited by applicant]
US 9750399B2 · Popovic · 2017 [cited by applicant]
US 10353271B2 · Wang et al. · 2019 [cited by applicant]
US 11989897B2 · Zhou · 2024 [cited by examiner]
US 20140192281A1 · Smithwick · 2014 [cited by examiner]
US 20210144357A1 · Kim · 2021 [cited by examiner]
US 20210183083A1 · Yan · 2021 [cited by examiner]
US 20210319569A1 · Peri · 2021 [cited by examiner]
US 20210366139A1 · Kim · 2021 [cited by examiner]
US 20210398313A1 · Lemley · 2021 [cited by examiner]
US 20220020112A1 · Wen · 2022 [cited by examiner]
US 20220067950A1 · Lv · 2022 [cited by examiner]
US 20220148207A1 · Varekamp · 2022 [cited by examiner]
US 20220198693A1 · Li · 2022 [cited by examiner]
US 20220358359A1 · Huang · 2022 [cited by examiner]
US 20220406013A1 · Xiong · 2022 [cited by examiner]
US 20230252661A1 · Zhu · 2023 [cited by examiner]
EP 3757890A1 · 2020 [cited by examiner]
WO WO2021259287A1 · 2021 [cited by examiner]
WO WO2022000266A1 · 2022 [cited by examiner]