A method for pose estimation of a moving object by computer calculation of displacements of the 3D position coordinates of the nodes of a skeleton model, that is continuously fitted into a sequence of 3D point clouds, the node coordinates being present in table form in an electronic memory and the 3D point clouds being determined from electronically recorded images of a depth sensor camera that represent the moving object, the skeleton model being a topology that exhibits as topology elements N 1 nodes, N 2 edges, and N 3 triangles having N 1, N 2 > 0 and N 3 ≥ 0, and each topology element being described by nodes, node pairs, or node triplets and being firmly assigned to a part of the moving object, and wherein an arithmetic unit carries out the following steps: a. randomly selecting a data point X of the 3D point cloud; b. calculating the crossing point P relative to X with reference to each topology element and identifying whether P lies in each case inside the topology element; c. calculating the distance from X to each topology element as the norm of the differential vector X - P; d. determining that topology element that exhibits the minimum distance from X among all topology elements whose crossing point P lies inside the topology element; e. displacing the topology element determined in step d by displacing all nodes establishing the topology element in the direction of the vector X - P, wherein the displacement vector for a node is multiplied by a learning rate and, in case the topology element is an edge or triangle, by a weight that results from the crossing point P relative to X with reference to the topology element determined in step d, and f. repeating the steps a to e for a predetermined number of learning steps while gradually reducing the learning rate; g. updating the node coordinates in the table of the electronic memory after K passes of the predetermined number of learning steps with K ≥ 1; h. providing at least the node coordinates updated in the table for further processing. a. randomly selecting a data point X of the 3D point cloud; b. calculating the crossing point P relative to X with reference to each topology element and identifying whether P lies in each case inside the topology element; c. calculating the distance from X to each topology element as the norm of the differential vector X - P; d. determining that topology element that exhibits the minimum distance from X among all topology elements whose crossing point P lies inside the topology element; e. displacing the topology element determined in step d by displacing all nodes establishing the topology element in the direction of the vector X - P, wherein the displacement vector for a node is multiplied by a learning rate and, in case the topology element is an edge or triangle, by a weight that results from the crossing point P relative to X with reference to the topology element determined in step d, and f. repeating the steps a to e for a predetermined number of learning steps while gradually reducing the learning rate; g. updating the node coordinates in the table of the electronic memory after K passes of the predetermined number of learning steps with K ≥ 1; h. providing at least the node coordinates updated in the table for further processing.