# US 11,908,080 B2 · Generating surfaces with arbitrary topologies using signed distance fields > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,908,080 B2 - Document ID: US11908080B2 - Status: Granted - Kind: B2 - Application number: 17/713,129 - Filed: 2022-04-04 - Published: 2023-10-05 - Granted: 2024-02-20 - Earliest claimed: 2022-04-04 - Assignees: Tencent America LLC - Applicants: Tencent America LLC - Inventors: Weikai Chen; Weiyang Li; Bo Yang - Web page: https://patentgrep.com/patent/US11908080B2 ## CPC - G06T 17/20: Finite element generation, e.g. wire-frame surface description, {tesselation} - G06V 20/64: Three-dimensional [3D] objects - G06T 7/50: Depth or shape recovery - G06T 19/006: {Mixed reality (object pose determination, tracking or camera calibration for mixed reality G06T7/00)} - G06V 10/46: Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features (colour feature extraction G06V10/56) - G06V 10/764: using classification, e.g. of video objects - G06V 10/7747: {Organisation of the process, e.g. bagging or boosting} - G06V 10/82: using neural networks - G06T 2200/08: involving all processing steps from image acquisition to 3D model generation - G06T 2207/10028: Range image; Depth image; 3D point clouds - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2210/56: Particle system, point based geometry or rendering - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract The various embodiments described herein include methods, devices, and systems for generating object meshes. In some embodiments, a method includes obtaining a trained classifier, and an input observation of a 3D object. The method further includes generating a three-pole signed distance field from the input observation using the trained classifier. The method also includes generating an output mesh of the 3D object from the three-pole signed distance field; and generating a display of the 3D object from the output mesh. ## Claims ### Claim 1 A method performed at a computing system having memory and one or more processors, the method comprising: obtaining a trained classifier; obtaining an input observation of a 3D object; generating a three-pole signed distance field from the input observation using the trained classifier, wherein generating the three-pole signed distance field from the input observation comprises assigning each point of a plurality of points with a value indicative of whether the point is inside a surface, outside the surface, or undefined; generating an output mesh of the 3D object from the three-pole signed distance field, wherein generating the output mesh comprises extracting surfaces from between sets of inside and outside values only; and generating a display of the 3D object from the output mesh. ### Claim 2 (depends on 1) The method of claim 1, further comprising obtaining a sampling point template; wherein the three-pole signed distance field is generated using the sampling point template. ### Claim 3 (depends on 2) The method of claim 2, wherein the three-pole signed distance field includes a three-pole signed distance value for each sampling point in the sampling point template. ### Claim 4 (depends on 1) The method of claim 1, wherein the input observation includes one or more open surfaces. ### Claim 5 (depends on 1) The method of claim 1, wherein generating the output mesh of the 3D object from the three-pole signed distance field comprises generating one or more open surfaces for the 3D object. ### Claim 6 (depends on 1) The method of claim 1, wherein the input observation is point cloud data. ### Claim 7 (depends on 1) The method of claim 1, wherein the input observation is an image. ### Claim 8 (depends on 1) The method of claim 1, wherein the output mesh is generated from the three-pole signed distance field using a marching cubes algorithm. ### Claim 9 (depends on 1) The method of claim 1, wherein the classifier is trained using a set of input sampling points and a corresponding input training observation. ### Claim 10 (depends on 9) The method of claim 9, wherein the set of input sampling points are generated by applying an octree construction to an input shape. ### Claim 11 (depends on 1) The method of claim 1, wherein the classifier is trained to learn respective surface functions for a set of input shapes. ### Claim 12 (depends on 1) The method of claim 1, wherein the classifier comprises a classification neural network. ### Claim 13 (depends on 1) The method of claim 1, wherein generating the display of the 3D object comprises generating a 2D view of the 3D object at a display device. ### Claim 14 (depends on 1) The method of claim 1, wherein generating the display of the 3D object comprises generating the display of the 3D object in an artificial-reality environment. ### Claim 15 A computing system, comprising: one or more processors; memory; and one or more programs stored in the memory and configured for execution by the one or more processors, the one or more programs comprising instructions for: obtaining a trained classifier; obtaining an input observation of a 3D object; generating a three-pole signed distance field from the input observation using the trained classifier, wherein generating the three-pole signed distance field from the input observation comprises assigning each point of a plurality of points with a value indicative of whether the point is inside a surface, outside the surface, or undefined; generating an output mesh of the 3D object from the three-pole signed distance field, wherein generating the output mesh comprises extracting surfaces from between sets of inside and outside values only; and generating a display of the 3D object from the output mesh. ### Claim 16 (depends on 15) The computing system of claim 15, wherein generating the output mesh of the 3D object from the three-pole signed distance field comprises generating one or more open surfaces for the 3D object. ### Claim 17 (depends on 15) The computing system of claim 15, wherein the one or more programs further comprise instructions for obtaining a sampling point template, wherein the three-pole signed distance field is generated using the sampling point template. ### Claim 18 A non-transitory computer-readable storage medium storing one or more programs configured for execution by a computing device having one or more processors, memory, and a display, the one or more programs comprising instructions for: obtaining a trained classifier; obtaining an input observation of a 3D object; generating a three-pole signed distance field from the input observation using the trained classifier, wherein generating the three-pole signed distance field from the input observation comprises assigning each point of a plurality of points with a value indicative of whether the point is inside a surface, outside the surface, or undefined; generating an output mesh of the 3D object from the three-pole signed distance field, wherein generating the output mesh comprises extracting surfaces from between sets of inside and outside values only; and generating a display of the 3D object from the output mesh. ### Claim 19 (depends on 18) The non-transitory computer-readable storage medium of claim 18, wherein generating the output mesh of the 3D object from the three-pole signed distance field comprises generating one or more open surfaces for the 3D object. ### Claim 20 (depends on 18) The non-transitory computer-readable storage medium of claim 18, wherein the one or more programs further comprise instructions for obtaining a sampling point template, wherein the three-pole signed distance field is generated using the sampling point template.