Abstract: A method for creating a digital 3D representation of a physical object, the physical object comprising an object surface; wherein the method comprises: obtaining input data, the input data including a plurality of captured images of the physical object and surface normal information of the object, the captured images being captured by an image capturing device, the surface normal information representing object surface normals associated with respective parts of the object surface; creating a digital 3D representation of the object surface; wherein creating the digital 3D representation is based on at least the obtained plurality of captured images and the obtained surface normal information.

Abstract: A method for creating a digital 3D representation of a physical object, the physical object comprising an object surface; wherein the method comprises: obtaining input data, the input data including a plurality of captured images of the physical object and surface normal information of the object, the captured images being captured by an image capturing device, the surface normal information representing object surface normals associated with respective parts of the object surface; creating a digital 3D representation of the object surface; wherein creating the digital 3D representation is based on at least the obtained plurality of captured images and the obtained surface normal information.

Abstract: A method based on Structure from Motion for processing a plurality of sparse images acquired by one or more acquisition devices to generate a sparse 3D points cloud and of a plurality of internal and external parameters of the acquisition devices includes the steps of collecting the images; extracting keypoints therefrom and generating keypoint descriptors; organizing the images in a proximity graph; pairwise image matching and generating keypoints connecting tracks according maximum proximity between keypoints; performing an autocalibration between image clusters to extract internal and external parameters of the acquisition devices, wherein calibration groups are defined that contain a plurality of image clusters and wherein a clustering algorithm iteratively merges the clusters in a model expressed in a common local reference system starting from clusters belonging to the same calibration group; and performing a Euclidean reconstruction of the object as a sparse 3D point cloud based on the extracted parame

Abstract: A method based on Structure from Motion for processing a plurality of sparse images acquired by one or more acquisition devices to generate a sparse 3D points cloud and of a plurality of internal and external parameters of the acquisition devices includes the steps of collecting the images; extracting keypoints therefrom and generating keypoint descriptors; organizing the images in a proximity graph; pairwise image matching and generating keypoints connecting tracks according maximum proximity between keypoints; performing an autocalibration between image clusters to extract internal and external parameters of the acquisition devices, wherein calibration groups are defined that contain a plurality of image clusters and wherein a clustering algorithm iteratively merges the clusters in a model expressed in a common local reference system starting from clusters belonging to the same calibration group; and performing a Euclidean reconstruction of the object as a sparse 3D point cloud based on the extracted parame