View source for 17821620. THREE-DIMENSIONAL POSE DETECTION BASED ON TWO-DIMENSIONAL SIGNATURE MATCHING simplified abstract (QUALCOMM Incorporated)

=THREE-DIMENSIONAL POSE DETECTION BASED ON TWO-DIMENSIONAL SIGNATURE MATCHING=


==Organization Name==

[[:Category:QUALCOMM Incorporated|QUALCOMM Incorporated]]


[[Category:QUALCOMM Incorporated]]

==Inventor(s)==

[[:Category:Ophir Paz of Floirac, 33 (FR)|Ophir Paz of Floirac, 33 (FR)]][[Category:Ophir Paz of Floirac, 33 (FR)]]

[[:Category:Gary Franklin Gimenez of Bordeaux 33 (FR)|Gary Franklin Gimenez of Bordeaux 33 (FR)]][[Category:Gary Franklin Gimenez of Bordeaux 33 (FR)]]

==THREE-DIMENSIONAL POSE DETECTION BASED ON TWO-DIMENSIONAL SIGNATURE MATCHING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17821620 titled 'THREE-DIMENSIONAL POSE DETECTION BASED ON TWO-DIMENSIONAL SIGNATURE MATCHING

==Simplified Explanation==

The patent application describes techniques for determining the pose of a three-dimensional deformable object using machine learning models and two-dimensional signatures.

* Inputs are provided to a machine learning model based on a computer-generated three-dimensional deformable object with a known pose.
* The machine learning model outputs a two-dimensional signature of the object based on the inputs.
* The two-dimensional signature is associated with the known pose of the computer-generated object.
* The pose of an actual three-dimensional deformable object is determined based on an image of the object and the associated two-dimensional signature.

### Potential Applications
* Augmented reality
* Virtual reality
* Robotics
* Animation

### Problems Solved
* Accurately determining the pose of deformable objects
* Improving object recognition and tracking

### Benefits
* Enhanced realism in virtual environments
* Improved object manipulation in robotics
* Streamlined animation processes


==Original Abstract Submitted==

Certain aspects of the present disclosure provide techniques for determining a pose of a three-dimensional deformable object. Embodiments include providing one or more inputs to a machine learning model based on a computer-generated three-dimensional deformable object that has a known pose. Embodiments include determining, based on one or more outputs from the machine learning model in response to the one or more inputs, a two-dimensional signature of the computer-generated three-dimensional deformable object. Embodiments include associating the two-dimensional signature with the known pose of the computer-generated three-dimensional deformable object. Embodiments include determining a respective pose of an actual three-dimensional deformable object based on an image of the actual three-dimensional deformable object and the associating.



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