Abstract
Many objects in real world have circular feature. It is a difficult task to obtain the 2D-3D pose estimation using circular feature in challenging scenarios. This paper proposes a method to incorporate elliptic shape prior for object pose estimation using a level set method. The relationship between the projection of the circular feature of a 3D object and the signed distance function corresponding to it is analyzed to yield a 2D elliptic shape prior. The method employs the combination of the grayscale histogram, the intensities of edge, and the smoothness distribution as main image feature descriptors that define the image statistical measure model. The elliptic shape prior combined with the image statistical measure energy model drives the elliptic shape contour to the projection of the circular feature of the 3D object with the current pose into the image plane. These works effectively reduce the impacts of the challenging scenarios on the pose estimate results. In addition, the method utilizes particle filters that take into account the motion dynamics of the object among scene frames, and this work provides the robust method for object 2D-3D pose estimation using circular feature in a challenging environment. Various numerical experiments are illustrated to show the performance and advantages of the proposed method.
Highlights
Pose estimation is an essential step in many machine vision and photogrammetric applications; the ultimate goal of pose estimation is to identify 3D pose of an object of interest from an image or image sequence [1, 2]
We propose an algorithm for object 2D-3D pose estimation using circular feature by exploiting elliptic shape constraint and image statistical measure
The elliptic shape prior combined with the image statistical measure energy model drives the elliptic shape contour to the projection of the circular feature of the 3D object with the current pose into the image plane
Summary
Pose estimation is an essential step in many machine vision and photogrammetric applications; the ultimate goal of pose estimation is to identify 3D pose of an object of interest from an image or image sequence [1, 2]. In [2] and [11], to utilize both framework above and to overcome their disadvantages, they extend them by incorporating a particle filter to exploit the underlying dynamics of system; this improvement provides the robust method for object 3D pose estimation in the presence of additive noise, complex background, and occlusion Both methods rely on the 3D model of the object to obtain prior information and construct a 6D pose parameter model to estimate the object pose. 4) The proposed algorithm yields high accuracy of object 2D-3D pose estimation using circular feature by processing a sequence of 2D monocular images degraded with additive noise, complex background, and partially occlusion.
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