USING ARTIFICIAL INTELLIGENCE TO OPTIMIZE SEAM PLACEMENT ON 3D MODELS
A method, computer system, and a computer program product for determining locations for seams on a 3D model of an object is provided. The present invention may include training an artificial intelligence model using a set of training data. The present invention may include generating a first model for the object using a shrink wrap method. The present invention may include generating a second model for the object using a decimation method. The present invention may include comparing the object to objects in the set of training data to identify an object in the training data having a similar shape. The present invention may include identifying the object by determining if the object fits in between the first and second models. The present invention may lastly include projecting seams onto a model of the object using the trained artificial intelligence model.
1 . A computer-implemented method for determining locations for seams on a 3D model of an object, comprising:
training an artificial intelligence model using a set of training data, the training data including a set of 3D models (each 3D model being defined by x, y, z coordinates) of a plurality of different objects, each 3D model of the set being comprised of a plurality of 2D maps (each 2D map being defined by u, v coordinates) joined together at one or more seams, wherein the seams were placed at locations on the 3D model deemed desirable by an artist;
generating a first model for the object using a shrink wrap method, wherein the first model includes a plurality of polygons;
generating a second model for the object using a decimation method, wherein the second model includes a plurality of polygons;
comparing the object to objects in the set of training data to identify an object in the training data having a similar shape;
identifying the object by determining if the object fits in between the first and second models; and
projecting seams onto a model of the object using the trained artificial intelligence model.
2 . The computer-implemented method of claim 1 , further comprising prompting a user to generate new seam vertices.
3 . The computer-implemented method of claim 1 , further comprising finding nearest vertices and marking the nearest vertices as seams.
4 . The computer-implemented method of claim 1 , wherein the shrink wrap method overestimates a volume of the object.
5 . The computer-implemented method of claim 1 , wherein the decimation method underestimates a volume of the object.
6 . The computer-implemented method of claim 1 , further comprising:
determining a similarity of the second model to an image of the object using an image processing technique.
7 . The computer-implemented method of claim 1 , wherein identifying the object by determining if the object fits in between the first and second models further comprises:
determining a confidence metric for the identification.
8 . A computer system for determining locations for seams on a 3D model of an object, comprising:
one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising:
training an artificial intelligence model using a set of training data, the training data including a set of 3D models (each 3D model being defined by x, y, z coordinates) of a plurality of different objects, each 3D model of the set being comprised of a plurality of 2D maps (each 2D map being defined by u, v coordinates) joined together at one or more seams, wherein the seams were placed at locations on the 3D model deemed desirable by an artist;
generating a first model for the object using a shrink wrap method, wherein the first model includes a plurality of polygons;
generating a second model for the object using a decimation method, wherein the second model includes a plurality of polygons;
comparing the object to objects in the set of training data to identify an object in the training data having a similar shape;
identifying the object by determining if the object fits in between the first and second models; and
projecting seams onto a model of the object using the trained artificial intelligence model.
9 . The computer system of claim 8 , further comprising prompting a user to generate new seam vertices.
10 . The computer system of claim 8 , further comprising finding nearest vertices and marking the nearest vertices as seams.
11 . The computer system of claim 8 , wherein the shrink wrap method overestimates a volume of the object.
12 . The computer system of claim 8 , wherein the decimation method underestimates a volume of the object.
13 . The computer system of claim 8 , further comprising:
determining a similarity of the second model to an image of the object using an image processing technique.
14 . The computer system of claim 8 , wherein identifying the object by determining if the object fits in between the first and second models further comprises:
determining a confidence metric for the identification.
15 . A computer program product for determining locations for seams on a 3D model of an object, comprising:
one or more non-transitory computer-readable storage media and program instructions stored on at least one of the one or more tangible storage media, the program instructions executable by a processor to cause the processor to perform a method comprising:
training an artificial intelligence model using a set of training data, the training data including a set of 3D models (each 3D model being defined by x, y, z coordinates) of a plurality of different objects, each 3D model of the set being comprised of a plurality of 2D maps (each 2D map being defined by u, v coordinates) joined together at one or more seams, wherein the seams were placed at locations on the 3D model deemed desirable by an artist;
generating a first model for the object using a shrink wrap method, wherein the first model includes a plurality of polygons;
generating a second model for the object using a decimation method, wherein the second model includes a plurality of polygons;
comparing the object to objects in the set of training data to identify an object in the training data having a similar shape;
identifying the object by determining if the object fits in between the first and second models; and
projecting seams onto a model of the object using the trained artificial intelligence model.
16 . The computer program product of claim 15 , further comprising prompting a user to generate new seam vertices.
17 . The computer program product of claim 15 , further comprising finding nearest vertices and marking the nearest vertices as seams.
18 . The computer program product of claim 15 , wherein the shrink wrap method overestimates a volume of the object.
19 . The computer program product of claim 15 , wherein the decimation method underestimates a volume of the object.
20 . The computer program product of claim 15 , further comprising:
determining a similarity of the second model to an image of the object using an image processing technique.