A method for predicting a three-dimensional representation in a 3D scene, using one or more two-dimensional representations obtained from at least one camera-generated image, the method comprising a step for obtaining a forest of regression trees, and for said at least one image: extracting a 2D representation associated with one or more positions within the image, wherein the method further comprises, for said at least one image: - predicting a 3D representation, corresponding to the extracted 2D representation, using the regression tree forest which comprises a set of possible associations between at least one 2D representation and a 3D representation, each possible association resulting from a predictive model, said predictive model of a possible association being defined by a distribution characterising the at least one 2D representation associated with the 3D representation for said possible association, and by the following parameters: - a first parameter representative of a status of the predictive model of the possible association; - a second parameter representative of a number of consecutive predictions evaluated as incorrect for the possible association; - a third parameter representative of the extracted 2D representations associated with the 3D representation by said possible association. evaluating one or more of the possible associations of the regression tree forest, according to a predetermined confidence criterion, in which the prediction can be evaluated for at least one possible association as being correct or incorrect according to the predetermined confidence criterion, and updating the regression tree forest comprising a deactivation of one or more possible associations, depending on the evaluation of said possible associations. extracting a 2D representation associated with one or more positions within the image, wherein the method further comprises, for said at least one image: - predicting a 3D representation, corresponding to the extracted 2D representation, using the regression tree forest which comprises a set of possible associations between at least one 2D representation and a 3D representation, each possible association resulting from a predictive model, said predictive model of a possible association being defined by a distribution characterising the at least one 2D representation associated with the 3D representation for said possible association, and by the following parameters: - a first parameter representative of a status of the predictive model of the possible association; - a second parameter representative of a number of consecutive predictions evaluated as incorrect for the possible association; - a third parameter representative of the extracted 2D representations associated with the 3D representation by said possible association. - predicting a 3D representation, corresponding to the extracted 2D representation, using the regression tree forest which comprises a set of possible associations between at least one 2D representation and a 3D representation, each possible association resulting from a predictive model, said predictive model of a possible association being defined by a distribution characterising the at least one 2D representation associated with the 3D representation for said possible association, and by the following parameters: - a first parameter representative of a status of the predictive model of the possible association; - a second parameter representative of a number of consecutive predictions evaluated as incorrect for the possible association; - a third parameter representative of the extracted 2D representations associated with the 3D representation by said possible association. - a first parameter representative of a status of the predictive model of the possible association; - a second parameter representative of a number of consecutive predictions evaluated as incorrect for the possible association; - a third parameter representative of the extracted 2D representations associated with the 3D representation by said possible association. evaluating one or more of the possible associations of the regression tree forest, according to a predetermined confidence criterion, in which the prediction can be evaluated for at least one possible association as being correct or incorrect according to the predetermined confidence criterion, and updating the regression tree forest comprising a deactivation of one or more possible associations, depending on the evaluation of said possible associations.