An information processing apparatus configured to paste a full-spherical panoramic image along an inner wall of a virtual three-dimensional sphere; calculate an arrangement position for arranging a planar image closer to a center point of the virtual three-dimensional sphere than the inner wall, in such an orientation that a line-of-sight direction from the center point to the inner wall and a perpendicular line of the planar image are parallel to each other, the planar image being obtained by pasting an embedding image to be embedded in the full-spherical panoramic image, on a two-dimensional plane; and display a display image on a display unit. The display image is a two-dimensional image viewed from the center point in the line-of-sight direction in a state in which the full-spherical panoramic image is pasted along the inner wall of the virtual three-dimensional sphere and the planar image is arranged at an arrangement position.

In one embodiment, a method includes receiving sensor data of a scene captured using one or more sensors, generating (1) a number of virtual surfaces representing a number of detected planar surfaces in the scene and (2) a point cloud representing detected features of objects in the scene based on the sensor data, assigning each point in the point cloud to one or more of the number of virtual surfaces, generating occupancy volumes for each of the number of virtual surfaces based on the points assigned to the virtual surface, generating a datastore including the number of virtual surfaces, the occupancy volumes of each of the number of virtual surfaces, and a spatial relationship between the number of virtual surfaces, receiving a query, and sending a response to the query, the response including an identified subset of the plurality of virtual surfaces in the datastore that satisfy the query.

A method using a two-dimensional (2D) image representation of three-dimensional (3D) geometric objects in a machine learning framework has been developed. The method includes generating a single 2D geometry image corresponding to a 3D object model, and providing the single geometry image as input to a shape analysis task to enable shape analysis of the 3D object model based only on information encoded in the single 2D geometry image in the machine learning framework.

A computer-implemented method for subject-specific two-dimensional modeling of a subject's vasculature may comprise: receiving a subject-specific three-dimensional model of the subject's vasculature, wherein the subject-specific three-dimensional model includes one or more centerlines; determining a two-dimensional viewing plane; determining a projection of the one or more centerlines of the subject-specific three-dimensional model onto the two-dimensional viewing plane; generating one or more models around the one or more centerlines; and generating a two-dimensional image depicting the one or more models.

An application sends primitives to a graphics processing system so that an image of a 3D scene can be rendered. The primitives are placed into primitive blocks for storage and retrieval from a parameter memory. Rather than simply placing the first primitives into a primitive block until the primitive block is full and then placing further primitives into the next primitive block, multiple primitive blocks can be “open” such that a primitive block allocation module can allocate primitives to one of the open primitive blocks to thereby sort the primitives into primitive blocks according to their spatial positions. By grouping primitives together into primitive blocks in accordance with their spatial positions, the performance of a rasterization module can be improved. For example, in a tile-based rendering system this may mean that fewer primitive blocks need to be fetched by a hidden surface removal module in order to process a tile.

Rendering system combines point sampling and volume sampling operations to produce rendering outputs. For example, to determine color information for a surface location in a 3-D scene, one or more point sampling operations are conducted in a volume around the surface location, and one or more sampling operations of volumetric light transport data are performed farther from the surface location. A transition zone between point sampling and volume sampling can be provided, in which both point and volume sampling operations are conducted. Data obtained from point and volume sampling operations can be blended in determining color information for the surface location. For example, point samples are obtained by tracing a ray for each point sample, to identify an intersection between another surface and the ray, to be shaded, and volume samples are obtained from a nested 3-D grids of volume elements expressing light transport data at different levels of granularity.

A computer-implemented method for performing at least one measurement in an anatomical vessel structure in an imaging region, the vessel structure comprising multiple vessels of interest for the measurement, the method comprises receiving a three-dimensional image data set of the imaging region; determining a two-dimensional unfolded image of the vessel structure from the image data set; displaying the unfolded image to the user; determining at least one landmark in the vessel structure and visualizing the at least one landmark at a corresponding landmark position in the unfolded image; performing the at least one measurement based on the at least one landmark and the three-dimensional image data set; and displaying the result of the at least one measurement in the unfolded image in a user presentation or together with the unfolded image in the user presentation.

An application sends primitives to a graphics processing system so that an image of a 3D scene can be rendered. The primitives are placed into primitive blocks for storage and retrieval from a parameter memory. Rather than simply placing the first primitives into a primitive block until the primitive block is full and then placing further primitives into the next primitive block, multiple primitive blocks can be “open” such that a primitive block allocation module can allocate primitives to one of the open primitive blocks to thereby sort the primitives into primitive blocks according to their spatial positions. By grouping primitives together into primitive blocks in accordance with their spatial positions, the performance of a rasterization module can be improved. For example, in a tile-based rendering system this may mean that fewer primitive blocks need to be fetched by a hidden surface removal module in order to process a tile.

A method for visualizing a three-dimensional volume for use in a virtual reality environment is performed by uploading two-dimensional images for evaluation, creating planar depictions of the two-dimensional images, and using thresholds to determine if voxels should be drawn. A voxel volume is created from the planar depictions and voxels. A user defines a plane to be used for slicing the voxel volume, and sets values of the plane location and plane normal. The slice plane is placed within the voxel volume and defines a desired remaining portion of the volumetric plane to be displayed. All but the desired remaining portion of the voxel volume is not drawn and the remaining portion is displayed.

A method and a system for determining an orthodontic treatment for a plurality of teeth of a subject are provided. The method comprises: receiving a 3D representation of a first tooth and a second tooth, adjacent thereto, of the subject, of a plurality of teeth of the subject; obtaining a tooth-gingiva segmentation loop; identifying an outer set of vertices positioned outside the tooth-gingiva segmentation loop and an inner set of vertices positioned inside the tooth-gingiva segmentation loop; generating a first interdental loop extending, along the 3D representation, through a given inner vertex and a given outer vertex, such that: the first interdental loop is at least partially indicative of an interdental boundary between the first tooth and the second tooth; and the first interdental loop intersects the tooth-gingiva segmentation loop; and generating the first tooth 3D representation based on the tooth-gingiva segmentation loop and the first interdental loop.