System and method for determining two-dimensional patches of three-dimensional object using machine learning models

A system for automatically determining a plurality of two-dimensional (3D) patches corresponding to a three-dimensional (3D) object using machine learning models for enabling an improved texture mapping process on the 3D object, wherein the system comprising:

• a server receives a mesh of the 3D object through a user device, wherein the mesh comprises a set of vertex positions or a set of vertices, a set of faces that connect the set of vertex positions, a set of normals, and a set of vertex normals, wherein the server comprises
• a memory that stores a set of instructions;
• and
• a processor that executes the set of instructions and is configured to,
• train a first machine learning model by providing a correlation between historic vertices with (a) historic faces, and (b) historic vertex normals of historic meshes based on an objective function of patch extraction as first training data;
• determine, using the first machine learning model, the plurality of 2D patches by partitioning the mesh until a distortion of the mesh reaches a threshold distortion, wherein each 2D patch comprises a subset of vertices, a subset of faces that are associated with the subset of vertices, and a subset of face normals, wherein the first machine learning model correlates the subset of vertices with (a) the subset of faces that are associated with the subset of vertices, and (b) the subset of face normals and is retrained using the correlation between the plurality of 2D patches with the first training data to improve determination of the plurality of 2D patches of the mesh;
• train a second machine learning model using (i) shape-related features of historic 2D patches associated with the historic meshes and (ii) an objective function of surface parameterization of the historic two-dimensional patches associated with the historic meshes as second training data;
• and
• automatically parameterize each vertex in the plurality of 2D patches to two-dimensional (2D) points on a two-dimensional (2D) plane to enable the texture mapping process on the 3D object, wherein the second machine learning model is retrained by providing a parameterized vertex of the plurality of 2D patches in the 2D points on the 2D plane to improve the texture mapping process further on the 3D object.

One or more non-transitory computer-readable storage mediums storing one or sequences of instructions, which when executed by one or more processors, causes a method for automatically determining a plurality of two-dimensional (2D) patches corresponding to a three-dimensional (3D) object using machine learning models for enabling an improved texture mapping process on the 3D object, wherein the method comprises:

• receiving a mesh of the 3D object through a user device, wherein the mesh comprises a set of vertex positions or a set of vertices, a set of faces that connect the set of vertex positions, a set of normals, and a set of vertex normals;
• training a first machine learning model by providing a correlation between historic vertices with (a) historic faces, and (b) historic vertex normals of historic meshes based on an objective function of patch extraction as first training data;
• determining, using the first machine learning model, the plurality of 2D patches by partitioning the mesh until a distortion of the mesh reaches a threshold distortion, wherein each 2D patch comprises a subset of vertices, a subset of faces that are associated with the subset of vertices, and a subset of face normals, wherein the first machine learning model correlates the subset of vertices with (a) the subset of faces that are associated with the subset of vertices, and (b) the subset of face normals and is retrained using the correlation between the plurality of 2D patches with the first training data to improve determination of the plurality of 2D patches of the mesh;
• training a second machine learning model using (i) shape-related features of historic 2D patches associated with the historic meshes and (ii) an objective function of surface parameterization of the historic two-dimensional patches associated with the historic meshes as second training data;
• and
• automatically parameterizing each vertex in the plurality of 2D patches to two-dimensional (2D) points on a two-dimensional (2D) plane to enable the texture mapping process on the 3D object, wherein the second machine learning model is retrained by providing a parameterized vertex of the plurality of 2D patches in the 2D points on the 2D plane to improve the texture mapping process further on the 3D object.