To measure the relative pose of moving non-cooperation textured objects in real time, a monocular simultaneous modeling and pose calculation method was proposed. A 3D covisibility model was incrementally constructed with frames containing the highest covisibility of features and best distribution to achieve cooperation between non-cooperation objects. Subsequently, the relative pose of the object was calculated via feature tracking by the motion prediction model. The mesh of the model was used to restore the 3D information of feature points that were distributed in an unknown area of the object surface. To reduce model error and improve the accuracy of pose estimation, bundle adjustment optimization was performed using a facet normal field, and the scale drift was reduced using closed-loop optimization. Experiments show that the method isa real-time online system that can recover 3D information of object in unstructured environments and accurately estimate relative poses in unstructured environments to provide technical support for 3D sensing and measurement modeling based on monocular vision. The mean reprojection error(MRE) of the features using the proposed method is less than 1.5 pixels, and the mean absolute error(MAE) of pose calculation is 4.29 mm and 1.54° while the average time consumption is less than 120 ms.