We introduce a system to assign navigation tasks to a self-moving robot using an Augmented Reality (AR) application running on a smartphone. The system relies on a robot controller and a central server hosted on a PC. The user points at a target location in the phone camera view and the robot moves accordingly. The robot and the phone are independently located in the 3D space thanks to registration methods running on the server, hence they do not need to be spatially registered to each other nor in direct line of sight.

Mesh texturing is an essential part of 3D scene reconstruction that enables a more realistic perception than the geometry alone and even compensates for inaccurate geometry. In this work we present a flexible formulation for color correction of textured scenes based on color augmentation per face. It can be employed as a post-processing step after selecting the best keyframe per face to compensate for color differences between pairs of neighboring faces. We present a Markov Random Field (MRF) formulation to find the best keyframes as well as the optimal color augmentations. We use a simple model to avoid reflection and camera vignetting during the view selection. Our model for color correction finds the piecewise-linear augmentation to be added to the texture patches of faces. It encourages smoothness inside every fragment while compensating color differences along view transitions. Moreover, we speed up the optimization by breaking down the formulation into multiple binary MRFs that estimate the best augmentations from coarse to fine resolutions. The experimental results prove our method outperforming the state of the art methods.

A multi-user experience extending a standard TV content with AR elements is presented. It runs with both a standard tablet and a premium MR headset, the Microsoft HoloLens. A virtual TV mosaic is displayed around the TV screen and used as a GUI to control both TV and MR content. This paper focuses on the collaborative and personalized dimension offered by the experience. Unlike most AR applications, it can be simultaneously run by several users using different devices. The users can share content with others while keeping a personalized display. The added-value of such an extended TV experience has been demonstrated through complementary types of content, and user feedback confirms a real interest in this new kind of home entertainment, at the same time immersive, interactive, collaborative and personalized.

A new method is described for automatically reconstructing 3D planar faces from multiple images of a scene. The novelty of the approach lies in the use of inter-image homographies to validate and best estimate the plane, and in the minimal initialization requirements-only a single 3D line with a textured neighbourhood is required to generate a plane hypothesis. The planar facets enable line grouping and also the construction of parts of the wireframe which were missed due to the inevitable shortcomings of feature detection and matching. The method allows a piecewise planar model of a scene to be built completely automatically, with no user intervention at any stage, given only the images and camera projection matrices as input. The robustness and reliability of the method are illustrated on several examples, from both aerial and interior views.

Presents a focusing strategy for the 3-D reconstruction of urban scenes from aerial stereo pairs. It consists in segmenting the scene into above-ground objects (buildings or vegetation), and it relies on 3-D and radiometric analyses. The classification is able to cope with extended above-ground, adjacent objects, slopes, and it is robust to image and scene variability.