Object separation for scanned assets
Various of the disclosed embodiments present systems and methods for distinguishing portions of a virtual model associated with a clothing article from portions of the virtual model not associated with the clothing article. Some embodiments facilitate quick and effective separation by employing a feature vector structure conducive to separation by a linear classifier. Such efficient separation may be especially beneficial in applications requiring the rapid scanning of large quantities of clothing while retaining high-fidelity representations of the clothing's geometry. Some embodiments further accommodate artist participation in the filtering process as well as scanning of articles from a variety of orientations and with a variety of supporting structures.
1. A computer system comprising:
at least one processor;
at least one interface configured to receive a scanned virtual model;
at least one memory comprising instructions configured to cause the system to perform a method separating a plurality of vertices associated with a clothing asset from a plurality of vertices associated with one or more objects other than the clothing asset, the method comprising:
receiving a first set of color values associated with the clothing asset;
receiving a second set of color values associated with a non-clothing object;
generating a first plurality of training feature vectors from the first set of color values;
generating a second plurality of training feature vectors from the second set of color values;
determining a plurality of feature component weights by providing the first plurality of feature vectors and the second plurality of feature vectors to a classifier;
receiving, via the at least one interface, the virtual model, the virtual model comprising:
a plurality of vertices, a first set of the plurality of vertices associated with clothing asset data and a second set of the plurality of vertices associated with non-clothing asset data; and
a map associating each vertex from the plurality of vertices with a corresponding color value; and
for a vertex of the plurality of vertices:
determining a color value associated with the vertex in the map;
generating a test feature vector based upon the color value;
applying the plurality of feature component weights to the test feature vector to determine a metric value; and
designating the vertex of the plurality of vertices for removal based upon the metric value.
2. The computer system of claim 1 , wherein the first plurality of training feature vectors, the second plurality of training feature vectors, and the test feature vector comprise:
a first component color value;
a second component color value;
a third component color value;
a squared value of the first component color value;
a squared value of the second component color value;
a squared value of the third component color value;
a multiplied value of the first component color value and the third component color value;
a multiplied value of the second component color value and the third component color value; and
a multiplied value of the first component color value and the second component color value.
3. The computer system of claim 2 , wherein the first component color value is a red value, the second component color value is a blue value, and the third component color value is a green value.
4. The computer system of claim 2 , wherein the classifier is a support vector machine.
5. The computer system of claim 4 , wherein
the support vector machine is a linear support vector machine, and wherein
applying the plurality of feature component weights to the test feature vector to determine a metric value comprises determining the dot product of the test feature vector with the plurality of feature component weights.
6. The computer system of claim 5 , wherein the non-clothing object is a mannequin.
7. The computer system of claim 6 , the method further comprising:
applying a rule from at least one of a plurality of system rules or a plurality of artist rules to the vertex designated for removal, the rule assessing color values associated with a plurality of vertices neighboring the vertex designated for removal; and
designating the vertex designated for removal to be retained based upon the application of the rule.
8. A non-transitory computer-readable medium comprising instructions configured to cause a computer system to perform a method comprising:
receiving a first set of color values associated with asset data;
receiving a second set of color values associated with non-asset data;
generating a first plurality of training feature vectors from the first set of color values;
generating a second plurality of training feature vectors from the second set of color values;
determining a plurality of feature component weights by providing the first plurality of feature vectors and the second plurality of feature vectors to a classifier;
receiving a virtual model comprising:
a plurality of vertices, a first set of the plurality of vertices associated with asset data and a second set of the plurality of vertices associated with non-asset data; and
a map associating each vertex from the plurality of vertices with a corresponding color value; and
for a vertex of the plurality of vertices:
determining a color value associated with the vertex in the map;
generating a test feature vector based upon the color value;
applying the plurality of feature component weights to the test feature vector to determine a metric value; and
designating the vertex of the plurality of vertices for removal based upon the metric value.
9. The non-transitory computer-readable medium of claim 8 , wherein the first plurality of training feature vectors, the second plurality of training feature vectors, and the test feature vector comprise:
a first component color value;
a second component color value;
a third component color value;
a squared value of the first component color value;
a squared value of the second component color value;
a squared value of the third component color value;
a multiplied value of the first component color value and the third component color value;
a multiplied value of the second component color value and the third component color value; and
a multiplied value of the first component color value and the second component color value.
10. The non-transitory computer-readable medium of claim 9 , wherein the first component color value is a red value, the second component color value is a blue value, and the third component color value is a green value.
11. The non-transitory computer-readable medium of claim 9 , wherein the classifier is a support vector machine.
12. The non-transitory computer-readable medium of claim 11 , wherein
the support vector machine is a linear support vector machine, and wherein
applying the plurality of feature component weights to the test feature vector to determine a metric value comprises determining the dot product of the test feature vector with the plurality of feature component weights.
13. The non-transitory computer-readable medium of claim 12 , wherein the asset is an article of clothing and the non-asset is a mannequin.
14. The non-transitory computer-readable medium of claim 13 , the method further comprising:
applying a rule from at least one of a plurality of system rules or a plurality of artist rules to the vertex designated for removal, the rule assessing color values associated with a plurality of vertices neighboring the vertex designated for removal; and
designating the vertex designed for removal to be retained based upon the application of the rule.
15. A computer-implemented method for separating asset data from non-asset data in a scanned virtual model, the method comprising:
receiving a first set of color values associated with asset data;
receiving a second set of color values associated with non-asset data;
generating a first plurality of training feature vectors from the first set of color values;
generating a second plurality of training feature vectors from the second set of color values;
determining a plurality of feature component weights by providing the first plurality of feature vectors and the second plurality of feature vectors to a classifier;
receiving a virtual model comprising:
a plurality of vertices, a first set of the plurality of vertices associated with asset data and a second set of the plurality of vertices associated with non-asset data; and
a map associating each vertex from the plurality of vertices with a corresponding color value; and
for a vertex of the plurality of vertices:
determining a color value associated with the vertex in the map;
generating a test feature vector based upon the color value;
applying the plurality of feature component weights to the test feature vector to determine a metric value; and
designating the vertex of the plurality of vertices for removal based upon the metric value.
16. The computer-implemented method of claim 15 , wherein the first plurality of training feature vectors, the second plurality of training feature vectors, and the test feature vector comprise:
a first component color value;
a second component color value;
a third component color value;
a squared value of the first component color value;
a squared value of the second component color value;
a squared value of the third component color value;
a multiplied value of the first component color value and the third component color value;
a multiplied value of the second component color value and the third component color value; and
a multiplied value of the first component color value and the second component color value.
17. The computer-implemented method of claim 16 , wherein the first component color value is a red value, the second component color value is a blue value, and the third component color value is a green value.
18. The computer-implemented method of claim 16 , wherein the classifier is a support vector machine.
19. The computer-implemented method of claim 18 , wherein
the support vector machine is a linear support vector machine, and wherein
applying the plurality of feature component weights to the test feature vector to determine a metric value comprises determining the dot product of the test feature vector with the plurality of feature component weights.
20. The computer-implemented method of claim 19 , wherein the asset is an article of clothing and the non-asset is a mannequin.