In this paper, we present an automatic pose estimation (6 DoF) technique to augment images using keyframes pre-registered to a CAD model. State of the art techniques recover the essential matrix (5 DoF) in an automatic manner, but include a manual step to align the image with the CAD reference system because the essential matrix does not provide the scale of the translation. We propose using planar structures to recover this scale automatically and to offer immediate augmentation. These techniques have been implemented in our augmented reality software. Qualitative tests are performed in an industrial environment.

Construction companies employ CAD software during the planning phase, but what is finally built often does not match the original plan. The procedure of validating the model is called "discrepancy check". The system proposed here allows the user to easily obtain an augmentation in order to find differences between the planned 3D model and the built items. The main difference to previous body of work in this field is the emphasis on usability and acceptance of the solution. While standard image-based solutions use markers or rely on a "perfect" 3D model to find the pose of the camera, our software uses anchor-plates. Anchor-Plates are rectangular structures installed on walls and ceiling in the majority of industrial edifices. We are using them as landmarks because they are the most reliable components often used as reference coordinates by constructors. Furthermore, for real industrial applications, they are the most suitable solutions in terms of general applicability. Unfortunately, they have not been designed with computer vision applications in mind. On the contrary, they are often made or painted in such way that they are not easily popping out. They are therefore difficult targets to segment and to track. This paper proposes a solution to extract and match them to their 3D counterparts. We created a software that uses the detected structures for pose estimation and image augmentation. The software has been successfully employed to find discrepancies in several rooms of two industrial plants.

We introduce a linear algorithm to recover the Euclidean motion between an orthographic and two perspective cameras from straight line correspondences filling the gap in the analysis of motion estimation from line correspondences for various projection models. The general relationship between lines in three views is described by the trifocal tensor. Euclidean structure from motion for three perspective views is a special case in which the relationship is defined by a collection of three matrices. Here, we describe the case of two calibrated perspective views and an orthographic view. Similar to the other cases, our linear algorithm requires 13 or more line correspondences to recover 27 coefficients of the trifocal tensor.

In this paper we introduce a linear algorithm to recover the Euclidean motion between one orthographic and two perspective cameras from straight line correspondences filling the gap in the analysis of motion estimation from line correspondences for various projection models. The general relationship between lines in three views is described by the trifocal tensor. Euclidean structure from motion for three perspective views is a special case in which the relationship is defined by a collection of three matrices. We describe the case of two calibrated perspective views and an orthographic view. Similar to the other cases, our linear algorithm requires 13 or more line correspondences to recover 27 coefficients of the trifocal tensor.

Despite tremendous progress in three dimensional modelling technology most sites in traditional industries do not have a computer model of their facilities at their disposal. Typically, 2D technical drawings are the most commonly used documents in these industries. In many cases a database of fully calibrated and oriented photogrammetric images of parts of the plant is also available. These images are often used for metric measurement and 3D as-built modelling. For planning revamps and maintenance, it is necessary to use industrial drawings as well as images and 3D models represented in a common "world" coordinate system. This paper proposes a method for full integration of technical drawings, calibrated images and as-built 3D models. A new algorithm is developed in order to use only few correspondences between points on a technical drawing and multiple images to estimate a metric planar transformation between the drawing and the world coordinate system. The paper describes the mathematical relationship between this transformation and the set of homographies needed for merging the technical drawing with all calibrated images. The method is implemented and fully integrated into an industrial software we developed for 3D as-built reconstruction. We present examples of a real application, in which the method is successfully applied to create an augmented reality representation of a waste water plant.

In this work, we use points, fines, and the linear extremal contours of cylinders to estimate the position and orientation of the camera in the world coordinate system. Other line-based pose estimation methods use the correspondences between 3D lines in space and 2D image lines, although the model and its observation are finite line segments. We present a noise model describing the probabilistic relationship between 3D lines and cylinders and their noisy observations. The noise model takes the finite nature of the observation into account. Position and orientation of cameras are estimated using a maximum-likelihood approach. Covariance matrices, confidence limits, and standard translation and rotation errors are estimated by singular value decomposition of the Jacobian matrix. The method provides clear indications on the reliability of each of the estimated parameters, and enables the user to add appropriate information, in terms of feature correspondence, to improve the accuracy if necessary. Simulation results are used to compare this method with some of the previously published ones. The algorithm is currently being used on real data for the update of 3D CAD models of industrial environments.