Object separation for scanned assets
AssigneeYouSpace, Inc.
InventorRalph Brunner
Life of this patent
Estimating the term…
Abstract
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.
Claims20
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.
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.
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.
Dependencies are inferred from the claim text.