A non-transitory computer readable medium that stores instructions that once executed by a computer cause the computer to execute the stages of: calculating a first function that represents an object that is three dimensional; calculating a second function that is a convolution or an approximated convolution of (a) the first function applied on points of the object, and (b) an other function that is the first function composed with a function that sends points of the object to opposite points; wherein the second function is translation invariant; and calculating the translation and rotation invariant features of the query object, based on the second function.

A method is disclosed for generating a component mesh of a component that may be built-up layer by layer in an additive manufacturing build-up process. The method includes providing a three-dimensional initial component mesh composed of initial mesh elements of uniform shape which include initial mesh nodes and initial mesh edges extending between the initial mesh nodes; slicing the initial component mesh by at least one cutting plane such that initial mesh elements are divided into at least two resulting mesh elements, wherein at the intersection points of the at least one cutting plane with edges of initial mesh elements resulting mesh nodes are defined; determining the position of each initial mesh element with respect to each cutting plane and thus which initial mesh element is divided into resulting mesh elements and which is not; and determining the shape of each resulting mesh element.

The present disclosure relates to a virtual object construction method and apparatus, and the method includes: obtaining a plurality of first planes on a target object in an environmental image according to an acquired environmental image; obtaining a plurality of corner points according to the plurality of first planes; constructing a virtual object corresponding to the target object according to a corner point located on the target object in the plurality of corner points.

An electronic device and method for shape refinement of a 3D mesh reconstructed from images is disclosed. A set of images of an object is acquired and used to estimate a first 3D mesh of a head portion of the object. A first set of operations is executed on the first 3D mesh to generate a second 3D mesh. The first set of operations includes a removal of one or more regions which are unneeded for head-shape estimation and/or a removal of one or more mesh artifacts associated with a 3D shape or a topology of the first 3D mesh. A 3D template mesh is processed to determine a set of filling patches which corresponds to a set of holes in the second 3D mesh. Based on the second 3D mesh and the set of filling patches, a hole filling operation is executed to generate a final 3D mesh.

An example of a non-transitory computer-readable medium storing machine-readable instructions. The instructions may cause the processor to receive a three-dimensional (3D) object representation and subdivide it into a triangular grid. Curved triangles may be calculated for triangles in the triangular grid. The triangles may be subdivided and differences calculated between corresponding sections of the curved triangles and the received 3D object representation.

A tessellation method uses both vertex tessellation factors and displacement factors defined for each vertex of a patch, which may be a quad, a triangle or an isoline. The method is implemented in a computer graphics system and involves calculating a vertex tessellation factor for each corner vertex in one or more input patches. Tessellation is then performed on the plurality of input patches using the vertex tessellation factors. The tessellation operation involves adding one or more new vertices and calculating a displacement factor for each newly added vertex. A world space parameter for each vertex is subsequently determined by calculating a target world space parameter for each vertex and then modifying the target world space parameter for a vertex using the displacement factor for that vertex.

There is provided a method for generating a 3D physical model of a patient specific anatomic feature from 2D medical images. The 2D medical images are uploaded by an end-user via a Web Application and sent to a server. The server processes the 2D medical images and automatically generates a 3D printable model of a patient specific anatomic feature from the 2D medical images using a segmentation technique. The 3D printable model is 3D printed as a 3D physical model such that it represents a 1:1 scale of the patient specific anatomic feature. The method includes the step of automatically identifying the patient specific anatomic feature.

A method for generating split-shapes. The method including generating a three dimensional (3D) wire frame of a shape that corresponds to an expression of a face. The method including generating a UV map that corresponds to the 3D wire frame. The method including identifying an isolated area of the shape using vertex weights or the UV map, wherein the isolated area corresponds to a portion of the face. The method including generating a weight map based on the vertex weights or the UV map, wherein the weight map identifies the isolated area. The method including generating a sub-shape of the shape based on the weight map, wherein the sub-shape is editable using an editing application.

An electronic apparatus performs a method of recovering a complete and dense point cloud from a partial point cloud. The method includes: constructing a sparse but complete point cloud from the partial point cloud through a contrastive teacher-student neural network; and transforming the sparse but complete point cloud to the complete and dense point cloud. In some embodiments, the contrastive teacher-student neural network has a dual network structure comprising a teacher network and a student network both sharing the same architecture. The teacher network is a point cloud self-reconstruction network, and the student network is a point cloud completion network.

An example computing system is configured to (i) generate a cross-sectional view of a three-dimensional drawing file; (ii) receive a first user input indicating a selection of a first mesh, wherein the selection comprises a selection point that establishes a first end point; (iii) generate a first representation indicating an alignment of the first end point with at least one corresponding geometric feature of the first mesh and a second representation indicating a set of one or more directions; (iv) receive a second user input indicating a given direction; (v) based on receiving the second user input, generate a dynamic representation of the dimensioning information along the given direction; (vi) receive a third user input indicating that the second user input is complete; (vii) based on receiving the third user input, add the dimensioning information to the cross-sectional view between the first end point and the second end point.