automatic pose estimation of uncalibrated multi-view images based on a planar object with a predefined contour model

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ID: 130684
2016
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Abstract
We have presented a framework to obtain camera pose (i.e., position and orientation in the 3D space) with real scale information of the uncalibrated multi-view images and the intrinsic camera parameters automatically. Our framework consists of two key steps. First, the initial value of the intrinsic camera and the pose parameters were extracted from homography estimation based on the contour model of some planar objects. Second, a refinement of the intrinsic camera and pose parameters was operated by the bundle adjustment procedure. Our framework can provide a complete flow of pose estimation of disorderly or orderly uncalibrated multi-view images, which can be used in vision tasks requiring scale information. Real multi-view images were utilized to demonstrate the robustness, flexibility and accuracy of the proposed framework. The proposed framework was also applied in 3D reconstruction.
Reference Key
li2016isprsautomatic Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Cailin Li;Langming Zhou;Wenhe Chen
Journal población y desarrollo
Year 2016
DOI
10.3390/ijgi5120244
URL
http://www.mdpi.com/2220-9964/5/12/244
Keywords
big data cloud computing pose estimation machine vision geoprocessing hadoopgeography (general)

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