Abstract: A method for estimating camera orientation relative to a ground surface. Line segments are detected from an image captured by a camera. A first virtual cube having three orthogonal vanishing points with a random 3D orientation is superimposed on to the image. The line segments of the image are classified grouped into 3D-directional groups. A second virtual cube is superimposed on to the image with an initial 3D orientation. An optimal 3D orientation of the second virtual cube is computed by iteratively changing the 3D orientation of the second virtual cube and measuring perpendicular distances of the three orthogonal vanishing points to the three line segment groups in each iteration starting with the initial 3D orientation, wherein the optimal 3D orientation of the second virtual cube being one that provides shortest perpendicular distances. Co-variances of the orthogonal vanishing points of the second virtual cube at the optimal orientation are computed.

Abstract: A method for estimating camera orientation relative to a ground surface. Line segments are detected from an image captured by a camera. A first virtual cube having three orthogonal vanishing points with a random 3D orientation is superimposed on to the image. The line segments of the image are classified grouped into 3D-directional groups. A second virtual cube is superimposed on to the image with an initial 3D orientation. An optimal 3D orientation of the second virtual cube is computed by iteratively changing the 3D orientation of the second virtual cube and measuring perpendicular distances of the three orthogonal vanishing points to the three line segment groups in each iteration starting with the initial 3D orientation, wherein the optimal 3D orientation of the second virtual cube being one that provides shortest perpendicular distances. Co-variances of the orthogonal vanishing points of the second virtual cube at the optimal orientation are computed.

Abstract: An iterative multi-image camera orientation estimation comprising: capturing an image of a scene before the camera; detecting line segments in the scene; computing a maximum likelihood (ML) camera orientation by maximizing a likelihood objective by rotating the camera's X-Y-Z coordinate system such that it is being optimally aligned with the line segments in at least two of the frontal, the lateral, and the vertical orthogonal directions; estimating a maximum a-posteriori (MAP) camera orientation that maximizes an a-posteriori objective such that the MAP camera orientation is an optimal value in between the priori camera orientation and the ML camera orientation, and is closer to the one with smaller uncertainty; iterating the multi-image camera orientation estimation with the priori camera orientation and its corresponding priori camera orientation uncertainty set to the computed MAP camera orientation and its corresponding uncertainty respectively until the uncertainty is lower than a threshold.