Removing outliers of large scale scene models based on automatic context analysis and convex optimization

This paper proposes a method for removing outliers of large scale scene model. First, the context of the scene images are analyzed. Some objects which may have negative effect should be removed. For instance, the sky often appear as background and moving object appear in most of scene images. They are also one of reasons that cause the outliers. Second, the constraints of image pair-wise are computed based on invariant features. The correspondence problem is solved by iterative method which remove the outliers. To avoid the disadvantage of incremental structure from motion, the global rotation of cameras are estimated by a robust method. These global rotations are fed to the point clouds generation procedure in third step. In contrast with using only canonical bundle adjustment which gain unstable structure in small baseline geometry and local minima, the proposed method utilized known-rotation framework combined bundle adjustment to generate accurate point clouds and camera positions with single global minimum. The patch based multi-view stereopsis is applied to dense point cloud upgrading. The simulation results will demonstrate the accuracy of this method from large scale scene images in outdoor environment.