View source for 17592500. CAMERA LOCALIZATION simplified abstract (MICROSOFT TECHNOLOGY LICENSING, LLC)

=CAMERA LOCALIZATION=


==Organization Name==

[[:Category:MICROSOFT TECHNOLOGY LICENSING, LLC|MICROSOFT TECHNOLOGY LICENSING, LLC]]


[[Category:MICROSOFT TECHNOLOGY LICENSING, LLC]]

==Inventor(s)==

[[:Category:Sudipta Narayan Sinha of Kirkland WA (US)|Sudipta Narayan Sinha of Kirkland WA (US)]][[Category:Sudipta Narayan Sinha of Kirkland WA (US)]]

[[:Category:Ondrej Miksik of Zurich (CH)|Ondrej Miksik of Zurich (CH)]][[Category:Ondrej Miksik of Zurich (CH)]]

[[:Category:Joseph Michael Degol of Seattle WA (US)|Joseph Michael Degol of Seattle WA (US)]][[Category:Joseph Michael Degol of Seattle WA (US)]]

[[:Category:Tien Do of Minneapolis MN (US)|Tien Do of Minneapolis MN (US)]][[Category:Tien Do of Minneapolis MN (US)]]

==CAMERA LOCALIZATION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17592500 titled 'CAMERA LOCALIZATION

==Simplified Explanation==

The patent application describes a method for camera localization within a scene using machine learning. Here are the key points:

* The method involves inputting an image of a scene captured by a camera into a machine learning model.
* The machine learning model has been trained to detect a variety of 3D scene landmarks that are pre-specified in a pre-built map of the scene.
* The machine learning model outputs predictions, which can be either a 2D location in the image representing one of the 3D scene landmarks or a 3D bearing vector pointing towards the predicted 3D location of a scene landmark.
* Using these predictions, the method computes an estimate of the camera's position and orientation within the pre-built map of the scene.

Potential applications of this technology:

* Augmented reality: This method can be used to accurately position virtual objects within a real-world scene, enhancing the user's augmented reality experience.
* Navigation and mapping: By accurately localizing the camera within a scene, this method can be used for navigation purposes or to create detailed maps of indoor or outdoor environments.
* Robotics: This technology can be applied to robotic systems to enable them to understand and navigate their surroundings more effectively.

Problems solved by this technology:

* Accurate camera localization: The method provides a reliable way to estimate the camera's position and orientation within a scene, even in complex or changing environments.
* Scene understanding: By detecting and localizing 3D scene landmarks, the method improves the camera's understanding of the scene, enabling more advanced applications such as augmented reality or robotic navigation.

Benefits of this technology:

* Improved accuracy: The use of machine learning allows for more precise localization of the camera within the scene, leading to better performance in applications like augmented reality or navigation.
* Flexibility: The method can be trained for different scenes, making it adaptable to various environments and scenarios.
* Real-time performance: The method can provide camera localization estimates in real-time, allowing for immediate feedback and interaction in applications like augmented reality or robotics.


==Original Abstract Submitted==

In various embodiments there is a method for camera localization within a scene. An image of a scene captured by the camera is input to a machine learning model, which has been trained for the particular scene to detect a plurality of 3D scene landmarks. The 3D scene landmarks are pre-specified in a pre-built map of the scene. The machine learning model outputs a plurality of predictions, each prediction comprising: either a 2D location in the image which is predicted to depict one of the 3D scene landmarks, or a 3D bearing vector, being a vector originating at the camera and pointing towards a predicted 3D location of one of the 3D scene landmarks. Using the predictions, an estimate of a position and orientation of the camera in the pre-built map of the scene is computed.



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