View source for Qualcomm incorporated (20240135559). DEPTH ESTIMATION USING IMAGE AND SPARSE DEPTH INPUTS simplified abstract

=DEPTH ESTIMATION USING IMAGE AND SPARSE DEPTH INPUTS=


==Organization Name==

[[:Category:qualcomm incorporated|qualcomm incorporated]]


[[Category:qualcomm incorporated]]

==Inventor(s)==

[[:Category:Georgi Dikov of Amsterdam (NL)|Georgi Dikov of Amsterdam (NL)]][[Category:Georgi Dikov of Amsterdam (NL)]]

[[:Category:Mohsen Ghafoorian of Diemen (NL)|Mohsen Ghafoorian of Diemen (NL)]][[Category:Mohsen Ghafoorian of Diemen (NL)]]

[[:Category:Joris Johannes Lambertus Van Vugt of Utrecht (NL)|Joris Johannes Lambertus Van Vugt of Utrecht (NL)]][[Category:Joris Johannes Lambertus Van Vugt of Utrecht (NL)]]

==DEPTH ESTIMATION USING IMAGE AND SPARSE DEPTH INPUTS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240135559 titled 'DEPTH ESTIMATION USING IMAGE AND SPARSE DEPTH INPUTS

==Simplified Explanation==

The patent application describes a method for generating depth information from images using a neural network model.

* Obtaining an image of a scene and depth information associated with objects in the scene.
* Processing the image and depth information using an encoder of a neural network model to generate a feature representation.
* Processing the feature representation using a decoder of the neural network model to generate a depth output corresponding to the image.

== Potential Applications ==
This technology could be applied in various fields such as:
* Autonomous driving for better understanding of the environment.
* Augmented reality for more realistic virtual overlays.

== Problems Solved ==
This technology addresses the following issues:
* Accurately estimating depth information from images.
* Enhancing the quality of depth perception in computer vision applications.

== Benefits ==
The benefits of this technology include:
* Improved accuracy in depth estimation.
* Enhanced performance in tasks requiring depth information.

== Potential Commercial Applications ==
The potential commercial applications of this technology include:
* Development of advanced camera systems for smartphones.
* Integration into robotics for better navigation capabilities.

== Possible Prior Art ==
One possible prior art for this technology could be the use of traditional computer vision techniques for depth estimation from images.

=== What are the limitations of this technology in real-world applications? ===

This technology may face limitations in scenarios with complex scenes or rapidly changing environments, where accurate depth estimation is challenging.

=== How does this technology compare to existing depth estimation methods? ===

This technology offers improved accuracy and efficiency compared to traditional methods, making it more suitable for real-time applications requiring depth information.


==Original Abstract Submitted==

systems and techniques are provided for generating depth information from one or more images. for instance, a method can include obtaining an image of a scene and obtaining depth information associated with one or more objects in the scene. the method can include processing, using an encoder of a neural network model, the image and the depth information to generate a feature representation of the image and the depth information. the method can further include processing, using a decoder of the neural network model, the feature representation of the image and the depth information to generate a depth output corresponding to the image.



[[Category:G06T7/50]]
[[Category:G06T7/73]]