7 results about "Texture model" patented technology

This invention relates to a texture primitive extraction method for tire X-ray images, addressing the challenge of accurately obtaining pixel-by-pixel texture information at the individual cord level while suppressing background interference. It falls under the field of computer vision and image processing technology. The method combines frequency domain analysis to obtain texture direction and spacing, utilizes this information to construct a mesh mask, and further combines background point extraction with real background reconstruction to filter out specific background regions in the original image. Under directional constraints, the remaining texture mesh is continuously tracked to obtain pixel-by-pixel texture information, thus achieving texture primitive extraction. This provides a more reliable foundation for subsequent pathological detection, structural analysis, and cord-level texture modeling.

This application discloses a method for rendering geological attribute meshes, including: obtaining geometric outer surface parameters; constructing a hollow model shell based on the geometric outer surface parameters; obtaining the coordinates of the hollow model shell; performing attribute mapping on the hollow model shell based on the coordinates of the hollow model shell to obtain a textured model; parsing the textured model to determine the geological body mesh on the textured model; merging the faces of geological body meshes with similar attributes on the textured model to obtain a low-precision model; and rendering the low-precision model to obtain a high-precision textured model. This method effectively reduces the number of geometric shapes such as triangles, simplifying the model, reducing the amount of rendering data, reducing the computational load, improving rendering efficiency, and resulting in better rendering effects. Because the attribute mapping uses high-precision attributes, it effectively ensures that the texture details and attribute information of the geological bodies are preserved.

A method is performed at a moveable scanner with one or more optical sensors. The method includes scanning, using the moveable scanner, an object having a surface. The scanning generates color data from a plurality of orientations of the moveable scanner with respect to the object. The method further includes generating, using at least the color data, a pixel map of the surface of the object, the pixel map including, for each respective pixel of a plurality of pixels: a color value of a corresponding point on the surface of the object; and a value for a non-color property of the corresponding point on the surface of the object.

The application relates to the technical field of three-dimensional modeling, and discloses a three-dimensional scene establishment method and system based on virtual and real scripts. The method comprises the following steps: performing deep semantic analysis according to a semantic description text, extracting emotional atmosphere and narrative logic features, obtaining a semantic feature vector, and matching and screening visual elements in a pre-stored visual element resource library to obtain a visual element list; generating spatial position and layout relationship of the visual elements according to the visual element list and a user interaction log, obtaining a layout parameter set, establishing a topological connection relationship, and generating a scene skeleton structure; performing mapping and synthesis of surface textures according to the scene skeleton structure to obtain a texture model; performing iterative adjustment of light and shadow parameters according to the texture model to obtain rendering configuration parameters; and performing three-dimensional scene drawing and pixel correction according to the rendering configuration parameters to obtain three-dimensional scene data. The method can generate a three-dimensional scene with high consistency of emotion and narrative logic according to semantic description.

This invention proposes a lightweight image super-resolution method based on superpixel guidance. The method includes: processing a low-resolution image using SLIC to obtain a superpixel label map; performing a convolutional mapping on the low-resolution image using lightweight convolution to obtain a shallow feature map; using the shallow feature map as input to a feature enhancement block to obtain the output feature map of an intermediate feature enhancement block; using the output feature map of the intermediate feature enhancement block as input to the next feature enhancement block, and stacking a predetermined number of feature enhancement blocks to obtain a deep feature map; using the deep feature map and the shallow feature map to obtain a final feature map; and obtaining a high-resolution image using the final feature map and the low-resolution image. This invention unifies "region-level context," "structure-sensitive sparse aggregation," and "fine-grained texture modeling" into a single lightweight module, achieving both structure preservation and detail recovery at a lower cost.