A measurement system includes a processor. Based on information measured by a camera that takes an image of a measurement target and an auxiliary object arranged on the measurement target, the processor acquires first point cloud data representing a three-dimensional geometry of the measurement target including the auxiliary object. Based on the first point cloud data and second point cloud data that is known and that represents a three-dimensional geometry of the measurement target, the processor eliminates point cloud data of the auxiliary object from the first point cloud data. The processor compares the first point cloud data, from which the point cloud data of the auxiliary object has been eliminated, with the second point cloud data. The processor displays information relating to a result of comparison on a display device.

A structured 3D model of a real-world object is generated from a series of 2D photographs of the object, using photogrammetry, a keypoint detection deep learning network (DLN), and retopology. In addition, object parameters of the object are received. A pressure map of the object is then generated by a pressure estimation DLN based on the structured 3D model and the object parameters. The pressure estimation DLN was trained on structured 3D models, object parameters, and pressure maps of a plurality of objects belonging to a given object category. The pressure map of the real-world object can be used in downstream processes, such as custom manufacturing.

A method for point cloud decoding includes receiving a bitstream. The method also includes decoding the bitstream into multiple frames that include pixels. Certain pixels of the multiple frames correspond to points of a three-dimensional (3D) point cloud. The multiple frames include a first set of frames that represent locations of the points of the 3D point cloud and a second set of frames that represent attribute information for the points of the 3D point cloud. The method further includes reconstructing the 3D point cloud based on the first set of frames. Additionally, the method includes identifying a first portion of the points of the reconstructed 3D point cloud based at least in part on a property associated with the multiple frames. The method also includes modifying a portion of the attribute information. The portion of the attribute information that is modified corresponds to the first portion of the points.

The present invention relates to medical image editing. In order to facilitate the medical image editing process, a medical image editing device (50) is provided that comprises a processor unit (52), an output unit (54), and an interface unit (56). The processor unit (52) is configured to provide a 3D surface model of an anatomical structure of an object of interest. The 3D surface model comprises a plurality of surface sub-portions. The surface sub-portions each comprise a number of vertices, and each vertex is assigned by a ranking value. The processor unit (52) is further configured to identify at least one vertex of vertices adjacent to the determined point of interest as an intended vertex. The identification is based on a function of a detected proximity distance to the point of interest and the assigned ranking value. The output unit (54) is configured to provide a visual presentation of the 3D surface model. The interface unit (56) is configured to determine a point of interest in the visual presentation of the 3D surface model by interaction of a user. The interface unit 56 is further configured to modify the 3D surface model by displacing the intended vertex by manual user interaction. In an example, the output unit (54) is a display configured to display the 3D surface model directly to the user (58).

A method of generating point cloud data from a laser scanning device, retaining a scanner pattern based on point cloud data, and generating an abbreviated mesh from the point cloud such that it can be faithfully restored to the original point cloud. The point cloud data must be structured such that azimuth, elevation, and range data can be extracted. The abbreviated mesh version of the point cloud is generated utilizing selected azimuth, elevation, and range data. Scanner patterns are generated utilizing the azimuth and elevation data. To faithfully regenerate the point cloud data from the abbreviated mesh, the mesh and the scanner pattern are cross referenced such that the regenerated point cloud has minimal data loss.

In some examples, a computing device can determine synthetic meshes based on source meshes of a source mesh sequence and target meshes of a target mesh sequence. The computing device can then place the respective synthetic meshes based at least in part on a rigid transformation to define a processor-generated character. For example, the computing device can determine subsets of the mesh sequences based on a similarity criterion. The computing device can determine modified first and second meshes having a connectivity corresponding to a reference mesh. The computing device can then determine the synthetic meshes based on the modified first and second meshes. In some examples, the computing device can project source and target textures onto the synthetic mesh to provide projected source and target textures. The computing device can determine a synthetic texture registered to the synthetic mesh based on the projected source and target textures.

A method is performed at a computing system for automatically generating an occluder, the method includes receiving an input model of the visual three-dimensional structure, the input model having a plurality of faces. The method includes simplifying the input model into an initial occluder including a plurality of candidate patches in a patch-based coarse mesh. The method includes determining a first quality metric of the initial occluder measured by a first number of pixels corresponding to objects behind the visual three-dimensional structure that are blocked by the input model and the initial occluder along a first view direction. The method includes removing one or more candidate patches associated with the first number of pixels from the initial occluder while maintaining the first quality metric above a first threshold to form the occluder for the visual three-dimensional structure.

Techniques of compressing level of detail (LOD) data involve sharing a texture image LOD among different mesh LODs for single-rate encoding. That is, a first texture image LOD corresponding to a first mesh LOD may be derived by refining a second texture image LOD corresponding to a second mesh LOD. This sharing is possible when texture atlases of LOD meshes are compatible.

A method, a system, an equipment and a medium for modifying the layering layer information of finite element model (FEM) unit. The method includes: obtaining a unit ID to be modified; determining the file to be modified according to the unit ID to be modified; receiving the new layering layer attribute ID regarding to the unit ID to be modified inputted by user; and replacing the original layering layer attribute ID corresponding to the unit ID to be modified in the file to be modified with the new layering layer attribute ID to complete the modifying the layering layer information of FEM unit. The method can directly modify the layering layer information of the units in the model file, without importing the model file into the pre-processing software to modify it and then exporting it again, thereby reducing the workload of technical staff and saving development time.

A system and method include performing digital block-out of one or more digital preparation teeth.