The present application discloses a method and apparatus for displaying a virtual landscape picture, a storage medium, and an electronic device. The method includes acquiring a plurality of landscape layer types comprised in the target landscape area to be displayed and determining a type weight corresponding to each landscape layer type in the plurality of landscape layer types; screening at least one target landscape layer type with a corresponding type weight which satisfies a condition from the plurality of landscape layer types, and acquiring an index value of a landscape layer corresponding to each of the screened at least one target landscape layer type; and sequentially storing the index value in each image color channel of the landscape layer index map to obtain the landscape layer index map corresponding to the target landscape area to be displayed.

A theme icon generation method includes obtaining an application icon, where the application icon includes a transparent region and an opaque region, and the opaque region includes an icon background and a first logo graphic; segmenting a first logo graphic from the opaque region; adjusting a size of the first logo graphic to generate a second logo graphic; and fusing the second logo graphic with a theme template to generate a theme icon

A theme icon generation method includes obtaining an application icon, where the application icon includes a transparent region and an opaque region, and the opaque region includes an icon background and a first logo graphic; segmenting a first logo graphic from the opaque region; adjusting a size of the first logo graphic to generate a second logo graphic; and fusing the second logo graphic with a theme template to generate a theme icon

Traditional systems that enable extracting information from Piping and Instrumentation Diagrams (P&IDs) lack accuracy due to existing noise in the images or require a significant volume of annotated symbols for training if deep learning models that provide good accuracy are utilized. Conventional few-shot/one-shot learning approaches require a significant number of training tasks for meta-training prior. The present disclosure provides a method and system that utilizes the one-shot learning approach that enables symbol recognition using a single instance per symbol class which is represented as a graph with points (pixels) sampled along the boundaries of different symbols present in the P&ID and subsequently, utilizes a Graph Convolutional Neural Network (GCNN) or a GCNN appended to a Convolutional Neural Network (CNN) for symbol classification. Accordingly, given a clean symbol image for each symbol class, all instances of the symbol class may be recognized from noisy and crowded P&IDs.

Traditional systems that enable extracting information from Piping and Instrumentation Diagrams (P&IDs) lack accuracy due to existing noise in the images or require a significant volume of annotated symbols for training if deep learning models that provide good accuracy are utilized. Conventional few-shot/one-shot learning approaches require a significant number of training tasks for meta-training prior. The present disclosure provides a method and system that utilizes the one-shot learning approach that enables symbol recognition using a single instance per symbol class which is represented as a graph with points (pixels) sampled along the boundaries of different symbols present in the P&ID and subsequently, utilizes a Graph Convolutional Neural Network (GCNN) or a GCNN appended to a Convolutional Neural Network (CNN) for symbol classification. Accordingly, given a clean symbol image for each symbol class, all instances of the symbol class may be recognized from noisy and crowded P&IDs.

Techniques for rendering two-dimensional vector graphics are described. The techniques include using a central processing unit to generate tessellate triangles along a vector path in which each of the tessellate triangles is represented by a set of vertices. From the tessellate triangles, an index buffer and a compressed vertex buffer are generated. The index buffer includes a vertex index for each vertex of each of the tessellate triangles. The compressed vertex buffer includes a vertex buffer entry for each unique vertex that maps to one or more vertex indices of the index buffer. The index buffer and the compressed vertex buffer are provided to a graphics processing unit to render the vector path with anti-aliasing.

Systems and methods and computer program products for processing three-dimensional (3D) graphics are provided. A method includes receiving 3D geometry data for a shape to be rendered to a display that comprises an array of hogels, the shape defined in a model space. The method can further include reducing downstream processing of the 3D geometry data to render the shape to the display, comprising identifying a subset of hogels in a hogel plane that have hogel bowtie frustums that intersect the shape.

A shadow rendering method for an image includes: re-projecting 3D coordinates of image pixels from an image space of the image to points on a 2D shadowmap space; estimating at least one of a horizontal and vertical distribution of the points in the shadow map space; for a flexible scale rasteriser ‘FSR’, updating a horizontal or vertical FSR curve corresponding to a distribution of FSR bins for the shadow map so that the corresponding horizontal or vertical distribution of points per bin is most even; and rendering the shadow map using flexible scale rasterization.

A shadow rendering method for an image includes: re-projecting 3D coordinates of image pixels from an image space of the image to points on a 2D shadowmap space; estimating at least one of a horizontal and vertical distribution of the points in the shadow map space; for a flexible scale rasteriser ‘FSR’, updating a horizontal or vertical FSR curve corresponding to a distribution of FSR bins for the shadow map so that the corresponding horizontal or vertical distribution of points per bin is most even; and rendering the shadow map using flexible scale rasterization.

Techniques related to graphics rendering including techniques for compression and/or decompression of graphics data by use of indexed subsets are described.