US 12,406,456 B2
Statistical shape and appearance modeling for volumetric geometry and intensity data
Rebecca Louise Bryan, Bishopsteignton (GB); David Richard Raymont, Devon (GB); Furqanullah Furqanullah, Exeter (GB); Christopher John Louis Goddard, Exeter (GB); and Mark Taylor, Adelaide (AU)
Assigned to Synopsys, Inc., Sunnyvale, CA (US)
Filed by Synopsys, Inc., Sunnyvale, CA (US)
Filed on Apr. 24, 2023, as Appl. No. 18/138,672.
Prior Publication US 2024/0355078 A1, Oct. 24, 2024
Int. Cl. G06T 19/20 (2011.01); G06T 7/50 (2017.01); G06V 10/74 (2022.01)
CPC G06T 19/20 (2013.01) [G06T 7/50 (2017.01); G06V 10/761 (2022.01); G06T 2219/2004 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A machine-implemented method, comprising:
receiving a reference mask, training masks, and training backgrounds, wherein the reference mask represents a 3-dimensional shape of a reference object, wherein the training masks represent external surfaces of training objects, wherein the training backgrounds comprise intensity data of the training objects, and wherein the intensity data represents one or more of color and texture;
re-orienting the training masks to align the training masks with the reference mask to provide re-orientation parameters for the training masks;
deforming the re-oriented training masks based on the reference mask to provide displacement fields indicative of differences between a 3-dimensional shape of the reference mask and 3-dimensional shapes of the re-oriented training masks;
re-orienting the training backgrounds based on the re-orientation parameters determined for the training masks to provide re-oriented training backgrounds;
deforming the re-oriented training backgrounds based on the displacement fields of the respective training masks to provide deformed training backgrounds;
combining the deformed training backgrounds and the displacement fields; and
reducing a dimensionality of the combined deformed training backgrounds and displacement fields to provide a statistical shape and appearance model (SSAM).