A Method for estimating the pose of a camera in a frame of reference of a three-dimensional scene, comprising the following steps: - Obtaining an image of colour intensities of the scene captured by the camera, so-called the current image; - Extracting a plurality of points of interest from the current image, one said point being invariant by geometric transformation of the image; - Forming a plurality of patches in the image of colour intensities, one patch comprising a point of interest of the extracted plurality; - Predicting the 3D locations of the points of interest of the plurality of patches in a frame of reference of the scene, by application of an automatic prediction system, said system having been trained with the aid of a training set comprising patches from a plurality of images of the scene acquired by the camera from a plurality of points of view, an image being associated in said set 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, the prediction step comprising an extraction of a feature vector per patch and the application of the automatic prediction system predicting a plurality of 3D locations per extracted feature vector; - Estimating a camera pose for the current image, by mapping the 2D positions of the plurality of points of interest and reprojections in the current image frame of reference of the predicted 3D locations, the automatic prediction system comprising a decision random forest, comprising decision trees, a decision tree comprising separator nodes, a said node being capable of binary separating a group of feature vectors from an upstream node into subgroups, transmitting the subgroups to downstream separator nodes and leaf nodes, capable of storing the 3D locations of the patches associated with a subgroup received from an upstream separator node, wherein the method comprises in a prior phase a step of training the prediction system comprising a binary separation test based on a calculation of distance between the vector of characteristics extracted from a patch of the training set and a vector of reference characteristics (VC refi) and a comparison of the calculated distance with respect to a predetermined threshold. - Obtaining an image of colour intensities of the scene captured by the camera, so-called the current image; - Extracting a plurality of points of interest from the current image, one said point being invariant by geometric transformation of the image; - Forming a plurality of patches in the image of colour intensities, one patch comprising a point of interest of the extracted plurality; - Predicting the 3D locations of the points of interest of the plurality of patches in a frame of reference of the scene, by application of an automatic prediction system, said system having been trained with the aid of a training set comprising patches from a plurality of images of the scene acquired by the camera from a plurality of points of view, an image being associated in said set 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, the prediction step comprising an extraction of a feature vector per patch and the application of the automatic prediction system predicting a plurality of 3D locations per extracted feature vector; - Estimating a camera pose for the current image, by mapping the 2D positions of the plurality of points of interest and reprojections in the current image frame of reference of the predicted 3D locations, the automatic prediction system comprising a decision random forest, comprising decision trees, a decision tree comprising separator nodes, a said node being capable of binary separating a group of feature vectors from an upstream node into subgroups, transmitting the subgroups to downstream separator nodes and leaf nodes, capable of storing the 3D locations of the patches associated with a subgroup received from an upstream separator node, wherein the method comprises in a prior phase a step of training the prediction system comprising a binary separation test based on a calculation of distance between the vector of characteristics extracted from a patch of the training set and a vector of reference characteristics (VC refi) and a comparison of the calculated distance with respect to a predetermined threshold. the automatic prediction system comprising a decision random forest, comprising decision trees, a decision tree comprising separator nodes, a said node being capable of binary separating a group of feature vectors from an upstream node into subgroups, transmitting the subgroups to downstream separator nodes and leaf nodes, capable of storing the 3D locations of the patches associated with a subgroup received from an upstream separator node, wherein the method comprises in a prior phase a step of training the prediction system comprising a binary separation test based on a calculation of distance between the vector of characteristics extracted from a patch of the training set and a vector of reference characteristics (VC refi) and a comparison of the calculated distance with respect to a predetermined threshold.