A method for recognizing instances of a 3D object in 3D scene data and scene intensity data and for determining the 3D poses of said instances comprising the following steps: (a) providing 3D object data and obtaining object intensity data; computing a model from the 3D object data and object intensity data, comprising the steps of selecting at least one sample point from the 3D object data, extracting object edge points from the object intensity data as object feature points, for each pair of selected sample point and extracted object feature point computing a point pair descriptor that includes the distance between the two points within the image plane of the intensity data and the relative orientations of the edge, the surface point normal and the difference vector between the two points, and creating a model that stores the point pairs indexed by the point pair descriptor; (b) providing 3D scene data and scene intensity data; (c) extracting scene edge points from the scene intensity data as scene feature points; (d) selecting at least one scene reference point from the 3D scene data; (e) computing, for each selected reference point, pose candidates for the 3D object under the assumption that said reference point is part of the 3D object by maximizing the number of scene point pairs that are similar with point pairs from the model (a 14) under a given pose candidate, comprising the steps selecting at least one scene feature point from the extracted scene feature points, pairing the selected scene reference point with selected scene feature points and computing for each point pair a descriptor that includes at least one of the distance between the two points within the image plane of the intensity data and the relative orientations of the edge, the surface point normal and the difference vector between the two points, finding for each pair in the scene, pairs from the model which are similar to said pair in the scene using the model computed in step and the descriptors computed in step, (f) computing a set of filtered poses from the pose candidates. (a) providing 3D object data and obtaining object intensity data; computing a model from the 3D object data and object intensity data, comprising the steps of selecting at least one sample point from the 3D object data, extracting object edge points from the object intensity data as object feature points, for each pair of selected sample point and extracted object feature point computing a point pair descriptor that includes the distance between the two points within the image plane of the intensity data and the relative orientations of the edge, the surface point normal and the difference vector between the two points, and creating a model that stores the point pairs indexed by the point pair descriptor; computing a model from the 3D object data and object intensity data, comprising the steps of selecting at least one sample point from the 3D object data, extracting object edge points from the object intensity data as object feature points, for each pair of selected sample point and extracted object feature point computing a point pair descriptor that includes the distance between the two points within the image plane of the intensity data and the relative orientations of the edge, the surface point normal and the difference vector between the two points, and creating a model that stores the point pairs indexed by the point pair descriptor; (b) providing 3D scene data and scene intensity data; (c) extracting scene edge points from the scene intensity data as scene feature points; (d) selecting at least one scene reference point from the 3D scene data; (e) computing, for each selected reference point, pose candidates for the 3D object under the assumption that said reference point is part of the 3D object by maximizing the number of scene point pairs that are similar with point pairs from the model (a 14) under a given pose candidate, comprising the steps selecting at least one scene feature point from the extracted scene feature points, pairing the selected scene reference point with selected scene feature points and computing for each point pair a descriptor that includes at least one of the distance between the two points within the image plane of the intensity data and the relative orientations of the edge, the surface point normal and the difference vector between the two points, finding for each pair in the scene, pairs from the model which are similar to said pair in the scene using the model computed in step and the descriptors computed in step, selecting at least one scene feature point from the extracted scene feature points, pairing the selected scene reference point with selected scene feature points and computing for each point pair a descriptor that includes at least one of the distance between the two points within the image plane of the intensity data and the relative orientations of the edge, the surface point normal and the difference vector between the two points, finding for each pair in the scene, pairs from the model which are similar to said pair in the scene using the model computed in step and the descriptors computed in step, (f) computing a set of filtered poses from the pose candidates.