In this demonstration, we present a general purpose Augmented Reality (AR) system that allows to add easily 3D computer generated (CG) objects into real man-made environments. Our system goes to a very intuitive and easy in situ 3D structure recovery of planar piecewise scenes without using powerful hardware nor commodity sensors. The user simply has to move the camera (translation of the camera is mandatory) and take two different pictures of the scene, and our approach obtains a rough planar piecewise representation of the environment suitable to conduct multi-planar tracking for visual model-based augmented reality and to augment it with virtual objects coherently. Polyhedral representations of scenes are very convenient for manmade environments indoor (e.g., offices, rooms, classrooms) and outdoor (e.g., facades, floor), hence we focus the potential applications of our system to augment simple rooms or urban scenes with virtual imagery.

This paper presents a novel non‐rigid multimodal registration method that relies on three basic steps: first, an initial approximation of the deformation field is obtained by a parametric registration technique based on particle filtering; second, an intensity mapping based on local variability measures (LVM) is applied over the two images in order to overcome the multimodal restriction between them; and third, an optical flow method is used in an iterative way to find the remaining displacements of the deformation field. Hence the new methodology offers a solution for multimodal NRR by a quadratic optimisation over a convex surface, which allows independent motion of each pixel, in contrast to methods that parameterise the deformation space. To evaluate the proposed method, a set of magnetic resonance/computed tomography clinical studies (pre‐ and post‐radiotherapy treatment) of three patients with cerebral tumour deformations of the brain structures was employed. The resulting registration was evaluated both qualitatively and quantitatively by standard indices of correspondence over anatomical structures of interest in radiotherapy (brain, tumour and cerebral ventricles). These results showed that one of the proposed LVM (entropy) offers a superior performance in estimating the non‐rigid deformation field.

In this work, we propose a computer vision-based AR system oriented to non-expert users that allows to add 3D computer generated (CG) objects into real man-made environments. Our system goes to a very intuitive and easy in situ 3D structure recovery of planar piecewise scenes without using powerful hardware nor commodity sensors. The user simply has to move the camera (translation of the focus is mandatory) and take two different pictures of the scene and our approach obtains a rough polyhedral representation of the environment suitable to conduct planar tracking for model-based augmented reality and to augment it with virtual objects coherently. Polyhedral representations of scenes are very convenient for offices and large rooms, hence we focus the potential applications of our system to augment offices and classrooms with virtual imagery.

This work confronts some theoretical camera models to reality and evaluates the suitability of these models for effective augmented reality (AR). It analyses what level of accuracy can be expected in real situations using a particular camera model and how robust the results are against realistic calibration errors. An experimental protocol is used that consists of taking images of a particular scene from different quality cameras mounted on a 4DOF micro-controlled device. The scene is made of a calibration target and three markers placed at different distances of the target. This protocol enables us to consider assessment criteria specific to AR as alignment error and visual impression, in addition to the classical camera positioning error.