Method of the Structure from Motion type for processing a plurality of sparse images acquired by one or more acquisition devices for the generation of a sparse 3D points cloud and of a plurality of internal and external parameters of the acquisition devices obtained by a processing of said images, comprising the following steps: a) collecting said images; b) extracting keypoints from each image and generating a descriptor for each keypoint ; c) organizing the images in a proximity graph based on keypoints descriptors similarity ; d) pairwise image matching starting from neighbor images as defined by the proximity graph and generating keypoints matches that connect similar keypoints on different images , according to the maximum proximity between the keypoints themselves; characterized in that it further comprises the following steps: e) performing an autocalibration between image clusters to extract the internal and external parameters of the acquisition devices , wherein a plurality of calibration groups is defined, each calibration group containing a plurality of image clusters, wherein the images are associated to EXIF parameters and the calibration groups are formed based on the similarity between the EXIF parameters of the images, and wherein a clustering algorithm is used such that the clusters are iteratively merged in a model expressed in a common local reference system, starting from clusters belonging to the same calibration group, wherein the autocalibration is based on the image keypoint matches extracted on step d); f) performing a Euclidean reconstruction of the object from all collected images in form of said sparse 3D point cloud after step e), wherein a clustering algorithm is used such that clusters of images are iteratively merged in a model expressed in a common local reference system, which clustering algorithm is re-run from the beginning on the images calibrated at step e), wherein the Euclidean reconstruction of the object is based on all the internal parameters extracted at step e) and the image keypoints matches extracted on step d). a) collecting said images; b) extracting keypoints from each image and generating a descriptor for each keypoint ; c) organizing the images in a proximity graph based on keypoints descriptors similarity ; d) pairwise image matching starting from neighbor images as defined by the proximity graph and generating keypoints matches that connect similar keypoints on different images , according to the maximum proximity between the keypoints themselves; characterized in that e) performing an autocalibration between image clusters to extract the internal and external parameters of the acquisition devices , wherein a plurality of calibration groups is defined, each calibration group containing a plurality of image clusters, wherein the images are associated to EXIF parameters and the calibration groups are formed based on the similarity between the EXIF parameters of the images, and wherein a clustering algorithm is used such that the clusters are iteratively merged in a model expressed in a common local reference system, starting from clusters belonging to the same calibration group, wherein the autocalibration is based on the image keypoint matches extracted on step d); f) performing a Euclidean reconstruction of the object from all collected images in form of said sparse 3D point cloud after step e), wherein a clustering algorithm is used such that clusters of images are iteratively merged in a model expressed in a common local reference system, which clustering algorithm is re-run from the beginning on the images calibrated at step e), wherein the Euclidean reconstruction of the object is based on all the internal parameters extracted at step e) and the image keypoints matches extracted on step d). <EOS>