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.

现实世界中的许多物体都具有圆形特征。在具有挑战性的场景中，使用圆形特征获取2D-3D姿态估计是一项艰巨的任务。本文提出了一种使用水平集方法将椭圆形先于对象姿态估计的方法。分析3D对象的圆形特征的投影和与其对应的有符号距离函数之间的关系，以先生成2D椭圆形。该方法采用灰度直方图，边缘强度和平滑度分布的组合作为定义图像统计度量模型的主要图像特征描述符。先验椭圆形与图像统计量度能量模型相结合，将椭圆形轮廓驱动到当前姿势进入图像平面的3D对象圆形特征的投影。这些工作有效地减少了挑战性情景对姿势估计结果的影响。另外，该方法利用考虑了场景帧之间对象运动的粒子过滤器，这项工作为在具有挑战性的环境中使用圆形特征的对象2D-3D姿态估计提供了可靠的方法。各种数值实验表明了该方法的性能和优点。该方法利用了粒子滤波器，该滤波器考虑了对象在场景帧之间的运动动态，这项工作为在具有挑战性的环境中使用圆形特征的对象2D-3D姿态估计提供了可靠的方法。各种数值实验表明了该方法的性能和优点。该方法利用了粒子滤波器，该滤波器考虑了对象在场景帧之间的运动动态，这项工作为在具有挑战性的环境中使用圆形特征的对象2D-3D姿态估计提供了可靠的方法。各种数值实验表明了该方法的性能和优点。