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

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

Organization Name

QUALCOMM Incorporated

Inventor(s)

Ophir Paz of Floirac, 33 (FR)

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.
      1. Potential Applications
  • Augmented reality
  • Virtual reality
  • Robotics
  • Animation
      1. Problems Solved
  • Accurately determining the pose of deformable objects
  • Improving object recognition and tracking
      1. 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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