# US 2021/0241430 A1 · Methods, devices, and computer program products for improved 3d mesh texturing > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241430 A1 - Document ID: US20210241430A1 - Status: Published application - Kind: A1 - Application number: 17/049,223 - Filed: 2018-09-13 - Published: 2021-08-05 - Earliest claimed: 2018-09-13 - Assignees: Sony Corporation - Applicants: Sony Corporation - Inventors: Fredrik MATTISSON; Pal SZASZ; Stefan KARLSSON - Web page: https://patentgrep.com/patent/US20210241430A1 ## CPC - G06T 5/70: Denoising; Smoothing - G06T 5/50: using two or more images, e.g. averaging or subtraction - G06T 11/10: Texturing; Colouring; Generation of textures or colours (retouching, inpainting or scratch removal G06T5/77) - G06T 15/04: Texture mapping - G06T 17/205: {Re-meshing} - G06T 2207/20221: Image fusion; Image merging - G06T 2207/20224: Image subtraction - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract Methods, systems, and computer program products for improving the generation of a 3D mesh texture include extracting a plurality of high frequency image components and a plurality of low frequency image components from a plurality of two-dimensional (2D) images of a three-dimensional (3D) object captured at respective points of perspective of the 3D object, generating a low frequency texture atlas from the plurality of low frequency image components, generating a high frequency texture atlas from the plurality of high frequency image components by performing a texturing operation comprising seam leveling on a subset of the plurality of high frequency image components, and generating the texture atlas by merging the low frequency texture atlas with the high frequency texture atlas. ## Claims ### Claim 1 A method of generating a texture atlas comprising: extracting a plurality of high frequency image components and a plurality of low frequency image components from a plurality of two-dimensional (2D) images of a three-dimensional (3D) object captured at respective points of perspective of the 3D object; generating a low frequency texture atlas from the plurality of low frequency image components; generating a high frequency texture atlas from the plurality of high frequency image components by performing a texturing operation comprising seam leveling on a subset of the plurality of high frequency image components; and generating the texture atlas by merging the low frequency texture atlas with the high frequency texture atlas. ### Claim 2 (depends on 1) The method of claim 1, wherein extracting the plurality of low frequency image components from the plurality of 2D images of the 3D object comprises performing a blurring operation on respective ones of the plurality of 2D images. ### Claim 3 (depends on 1) The method of claim 1, wherein extracting the plurality of high frequency image components from the plurality of 2D images comprises subtracting respective ones of the low frequency image components from respective ones of the plurality of 2D images. ### Claim 4 (depends on 1) The method of claim 1, further comprising: extracting a plurality of high frequency intermediate image components from the plurality of 2D images; extracting a plurality of middle frequency intermediate image components from the plurality of 2D images; and extracting a plurality of low frequency intermediate image components from the plurality of 2D images, wherein extracting the plurality of high frequency image components comprises merging the plurality of high frequency intermediate image components and the plurality of middle frequency intermediate image components, and wherein generating the plurality of low frequency image components comprises merging the plurality of low frequency intermediate image components and the plurality of middle frequency intermediate image components. ### Claim 5 (depends on 4) The method of claim 4, further comprising: generating a plurality of first blurred images by performing a blurring operation on respective ones of the plurality of 2D images, and generating a plurality of second blurred images by performing the blurring operation on respective ones of the plurality of first blurred images. ### Claim 6 (depends on 5) The method of claim 5, wherein extracting the plurality of low frequency intermediate image components from the plurality of 2D images comprises selecting the plurality of second blurred images, wherein extracting the plurality of middle frequency intermediate image components from the plurality of 2D images comprises subtracting respective ones of the plurality of second blurred images from respective ones of the plurality of first blurred images, and wherein extracting the plurality of high frequency intermediate image components from the plurality of 2D images comprises subtracting respective ones of the plurality of first blurred images from respective ones of the plurality of 2D images. ### Claim 7 (depends on 1) The method of claim 1, wherein a first number of the subset of the plurality of high frequency image components is less than a second number of the plurality of low frequency image components. ### Claim 8 (depends on 1) The method of claim 1, further comprising selecting a first high frequency image component of the plurality of high frequency image components as part of the subset of the plurality of high frequency image components based on a quality of the first high frequency image component, an orientation of the first high frequency image component with respect to the 3D object, and/or a distance to the 3D object from which the first high frequency image component was captured. ### Claim 9 (depends on 1) The method of claim 1, wherein the texturing operation comprising seam leveling comprises a Markov random field optimization operation. ### Claim 10 (depends on 1) The method of any of claim 1, wherein generating the low frequency texture atlas based on the plurality of low frequency image components comprises summing, for each low frequency image component of the plurality of low frequency image components, a color value of the low frequency image component multiplied by a weight value. ### Claim 11 (depends on 1) A computer program product for operating an imaging system, the computer program product comprising a non-transitory computer readable storage medium having computer readable program code embodied in the medium that when executed by a processor causes the processor to perform the method of claim 1. ### Claim 12 A system for processing images, the system comprising: a processor; and a memory coupled to the processor and storing computer readable program code that when executed by the processor causes the processor to perform operations comprising: extracting a plurality of high frequency image components and a plurality of low frequency image components from a plurality of two-dimensional (2D) images of a three-dimensional (3D) object captured at respective points of perspective of the 3D object; generating a low frequency texture atlas from the plurality of low frequency image components; generating a high frequency texture atlas from the plurality of high frequency image components by performing a texturing operation comprising seam leveling on a subset of the plurality of high frequency image components; and generating a texture atlas by merging the low frequency texture atlas with the high frequency texture atlas. ### Claim 13 (depends on 12) The system of claim 12, wherein extracting the plurality of low frequency image components from the plurality of 2D images of the 3D object comprises performing a blurring operation on respective ones of the plurality of 2D images. ### Claim 14 (depends on 12) The system of claim 12, wherein extracting the plurality of high frequency image components from the plurality of 2D images comprises subtracting respective ones of the low frequency image components from respective ones of the plurality of 2D images. ### Claim 15 (depends on 12) The system of claim 12, wherein the operations further comprise: extracting a plurality of high frequency intermediate image components from the plurality of 2D images; extracting a plurality of middle frequency intermediate image components from the plurality of 2D images; and extracting a plurality of low frequency intermediate image components from the plurality of 2D images, wherein extracting the plurality of high frequency image components comprises merging the plurality of high frequency intermediate image components and the plurality of middle frequency intermediate image components, and wherein generating the plurality of low frequency image components comprises merging the plurality of low frequency intermediate image components and the plurality of middle frequency intermediate image components. ### Claim 16 (depends on 15) The system of claim 15, wherein the operations further comprise: generating a plurality of first blurred images by performing a blurring operation on respective ones of the plurality of 2D images, and generating a plurality of second blurred images by performing the blurring operation on respective ones of the plurality of first blurred images. ### Claim 17 (depends on 16) The system of claim 16, wherein extracting the plurality of low frequency intermediate image components from the plurality of 2D images comprises selecting the plurality of second blurred images, wherein extracting the plurality of middle frequency intermediate image components from the plurality of 2D images comprises subtracting respective ones of the plurality of second blurred images from respective ones of the plurality of first blurred images, and wherein extracting the plurality of high frequency intermediate image components from the plurality of 2D images comprises subtracting respective ones of the plurality of first blurred images from respective ones of the plurality of 2D images. ### Claim 18 (depends on 12) The system of claim 12, wherein a first number of the subset of the plurality of high frequency image components is less than a second number of the plurality of low frequency image components. ### Claim 19 (depends on 12) The system of claim 12, wherein the operations further comprise selecting a first high frequency image component of the plurality of high frequency image components as part of the subset of the plurality of high frequency image components based on a quality of the first high frequency image component, an orientation of the first high frequency image component with respect to the 3D object, and/or a distance to the 3D object from which the first high frequency image component was captured. ### Claim 20 (canceled) ### Claim 21 (depends on 12) The system of claim 12, wherein generating the low frequency texture atlas based on the plurality of low frequency image components comprises summing, for each low frequency image component of the plurality of low frequency image components, a color value of the low frequency image component multiplied by a weight value.