Abstract: A method for processing images of a scene including a surface made of a material of unknown reflectance comprises the following steps: from at least 3 different positions of an image sensor, the positions and corresponding orientations of the sensor known, acquiring images of the scene illuminated by a light source, each image containing specularity generated by reflection of the light source from the surface and depending on the position, the shape and the intensity of the light source and the reflectance of the material; in each image, detecting each specularity and for each specularity, estimating a conic approximating the specularity. It comprises constructing a quadric representing the position, intensity and shape of the light source and the reflectance of the material, on the basis of the conics, and of the position and orientation of the image sensor during the acquisition of the images containing the specularities respectively approximated by these conics.

Abstract: A method for processing images of a scene including a surface made of a material of unknown reflectance comprises the following steps: from at least 3 different positions of an image sensor, the positions and corresponding orientations of the sensor known, acquiring images of the scene illuminated by a light source, each image containing specularity generated by reflection of the light source from the surface and depending on the position, the shape and the intensity of the light source and the reflectance of the material; in each image, detecting each specularity and for each specularity, estimating a conic approximating the specularity. It comprises constructing a quadric representing the position, intensity and shape of the light source and the reflectance of the material, on the basis of the conics, and of the position and orientation of the image sensor during the acquisition of the images containing the specularities respectively approximated by these conics.

Abstract: A method and a system are provided integrating inter-pose constraints, which are weighted in a step of “bundle adjustment” in a localization application (SLAM). The inertial data associated with a motion model serve to predict the pose of the system at the instant of a new image. This prediction is then used to construct an inter-pose constraint. A technique arising from learning theory selects the weighting coefficient (ratio of the noise of the covariance signals) in an efficient manner. The inter-pose constraint thus weighted is then integrated within the cost function of the bundle adjustment.

Abstract: A method and a system are provided integrating inter-pose constraints, which are weighted in a step of “bundle adjustment” in a localization application (SLAM). The inertial data associated with a motion model serve to predict the pose of the system at the instant of a new image. This prediction is then used to construct an inter-pose constraint. A technique arising from learning theory selects the weighting coefficient (ratio of the noise of the covariance signals) in an efficient manner. The inter-pose constraint thus weighted is then integrated within the cost function of the bundle adjustment.