Online coupled camera pose estimation and dense reconstruction from video
Abstract
A product may receive each image in a stream of video image of a scene, and before processing the next image, generate information indicative of the position and orientation of an image capture device that captured the image at the time of capturing the image. The product may do so by identifying distinguishable image feature points in the image; determining a coordinate for each identified image feature point; and for each identified image feature point, attempting to identify one or more distinguishable model feature points in a three dimensional (3D) model of at least a portion of the scene that appears likely to correspond to the identified image feature point. Thereafter, the product may find each of the following that, in combination, produce a consistent projection transformation of the 3D model onto the image: a subset of the identified image feature points for which one or more corresponding model feature points were identified; and, for each image feature point that has multiple likely corresponding model feature points, one of the corresponding model feature points. The product may update a 3D model of at least a portion of the scene following the receipt of each video image and before processing themore »
- Inventors:
- Issue Date:
- Research Org.:
- Univ. of Southern California, Los Angeles, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1330704
- Patent Number(s):
- 9483703
- Application Number:
- 14/120,370
- Assignee:
- UNIVERSITY OF SOUTHERN CALIFORNIA
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06T - IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- DOE Contract Number:
- FG52-08NA28775
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 2014 May 14
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 99 GENERAL AND MISCELLANEOUS; 97 MATHEMATICS AND COMPUTING
Citation Formats
Medioni, Gerard, and Kang, Zhuoliang. Online coupled camera pose estimation and dense reconstruction from video. United States: N. p., 2016.
Web.
Medioni, Gerard, & Kang, Zhuoliang. Online coupled camera pose estimation and dense reconstruction from video. United States.
Medioni, Gerard, and Kang, Zhuoliang. Tue .
"Online coupled camera pose estimation and dense reconstruction from video". United States. https://www.osti.gov/servlets/purl/1330704.
@article{osti_1330704,
title = {Online coupled camera pose estimation and dense reconstruction from video},
author = {Medioni, Gerard and Kang, Zhuoliang},
abstractNote = {A product may receive each image in a stream of video image of a scene, and before processing the next image, generate information indicative of the position and orientation of an image capture device that captured the image at the time of capturing the image. The product may do so by identifying distinguishable image feature points in the image; determining a coordinate for each identified image feature point; and for each identified image feature point, attempting to identify one or more distinguishable model feature points in a three dimensional (3D) model of at least a portion of the scene that appears likely to correspond to the identified image feature point. Thereafter, the product may find each of the following that, in combination, produce a consistent projection transformation of the 3D model onto the image: a subset of the identified image feature points for which one or more corresponding model feature points were identified; and, for each image feature point that has multiple likely corresponding model feature points, one of the corresponding model feature points. The product may update a 3D model of at least a portion of the scene following the receipt of each video image and before processing the next video image base on the generated information indicative of the position and orientation of the image capture device at the time of capturing the received image. The product may display the updated 3D model after each update to the model.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2016},
month = {11}
}
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