Abstract: A method for predicting a three-dimensional representation in a three-dimensional scene, using one or more two-dimensional representations, obtained from at least one image generated by a camera. The method includes obtaining a forest of regression trees, and for the at least one image: extracting a 2D representation associated with one or more positions within the image; predicting a 3D representation, corresponding to the extracted 2D representation, using the regression tree forest includes a set of possible associations between at least one 2D representation and a 3D representation, each possible association resulting from a predictive model; evaluating one or more of the possible associations of the regression tree forest, according to a predetermined confidence criterion; and updating the regression tree forest including a deactivation of one or more possible associations, depending on the evaluation of the possible associations.

Abstract: A method for estimating a camera pose in a frame of reference of a three-dimensional scene, including: obtaining an image of colour intensities of the scene; extracting points of interest, which are invariant by geometric transformation of the image; forming patches in the image, each including an extracted point of interest; predicting 3D locations of the points of interest by applying an automatic prediction system, trained by a training set including patches from images acquired from plural points of view, an image being associated with a 2D position of its point of interest in a frame of reference of the image and with a 3D position of its point of interest in the frame of reference of the scene; estimating the pose of a camera, by mapping the 2D positions of the points of interest and reprojections in the current image frame of reference of the predicted 3D locations.

Abstract: A method for predicting a three-dimensional representation in a three-dimensional scene, using one or more two-dimensional representations, obtained from at least one image generated by a camera. The method includes obtaining a forest of regression trees, and for the at least one image: extracting a 2D representation associated with one or more positions within the image; predicting a 3D representation, corresponding to the extracted 2D representation, using the regression tree forest includes a set of possible associations between at least one 2D representation and a 3D representation, each possible association resulting from a predictive model; evaluating one or more of the possible associations of the regression tree forest, according to a predetermined confidence criterion; and updating the regression tree forest including a deactivation of one or more possible associations, depending on the evaluation of the possible associations.

Abstract: A method for estimating a camera pose in a frame of reference of a three-dimensional scene, including: obtaining an image of colour intensities of the scene; extracting points of interest, which are invariant by geometric transformation of the image; forming patches in the image, each including an extracted point of interest; predicting 3D locations of the points of interest by applying an automatic prediction system, trained by a training set including patches from images acquired from plural points of view, an image being associated with a 2D position of its point of interest in a frame of reference of the image and with a 3D position of its point of interest in the frame of reference of the scene; estimating the pose of a camera, by mapping the 2D positions of the points of interest and reprojections in the current image frame of reference of the predicted 3D locations.