View source for 18067183. DEPTH ESTIMATION FOR MONOCULAR SYSTEMS USING ADAPTIVE GROUND TRUTH WEIGHTING simplified abstract (QUALCOMM Incorporated)

=DEPTH ESTIMATION FOR MONOCULAR SYSTEMS USING ADAPTIVE GROUND TRUTH WEIGHTING=


==Organization Name==

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


[[Category:QUALCOMM Incorporated]]

==Inventor(s)==

[[:Category:Amin Ansari of Federal Way WA (US)|Amin Ansari of Federal Way WA (US)]][[Category:Amin Ansari of Federal Way WA (US)]]

[[:Category:Sai Madhuraj Jadhav of San Diego CA (US)|Sai Madhuraj Jadhav of San Diego CA (US)]][[Category:Sai Madhuraj Jadhav of San Diego CA (US)]]

[[:Category:Yunxiao Shi of San Diego CA (US)|Yunxiao Shi of San Diego CA (US)]][[Category:Yunxiao Shi of San Diego CA (US)]]

[[:Category:Gautam Sachdeva of San Diego CA (US)|Gautam Sachdeva of San Diego CA (US)]][[Category:Gautam Sachdeva of San Diego CA (US)]]

[[:Category:Avdhut Joshi of San Marcos CA (US)|Avdhut Joshi of San Marcos CA (US)]][[Category:Avdhut Joshi of San Marcos CA (US)]]

==DEPTH ESTIMATION FOR MONOCULAR SYSTEMS USING ADAPTIVE GROUND TRUTH WEIGHTING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18067183 titled 'DEPTH ESTIMATION FOR MONOCULAR SYSTEMS USING ADAPTIVE GROUND TRUTH WEIGHTING

'''Simplified Explanation:'''
This patent application describes systems, methods, and devices for vehicle driving assistance systems that utilize image processing. The technology involves receiving and comparing predicted and measured depth maps to improve driving assistance capabilities.

* The computing device receives a predicted depth map and a measured depth map.
* It determines a time difference between the predicted and measured depth maps.
* A supervision loss term is calculated based on the time difference, potentially weighted for accuracy.
* The model is trained based on the supervision loss term to enhance the accuracy of the predicted depth map.

'''Key Features and Innovation:'''
* Utilizes image processing for vehicle driving assistance systems.
* Compares predicted and measured depth maps to improve accuracy.
* Calculates a supervision loss term based on the time difference.
* Trains a model to enhance the accuracy of predicted depth maps.

'''Potential Applications:'''
This technology can be applied in various vehicle driving assistance systems to enhance safety and accuracy in navigation and obstacle detection.

'''Problems Solved:'''
This technology addresses the challenge of improving the accuracy of predicted depth maps in vehicle driving assistance systems.

'''Benefits:'''
* Enhanced accuracy in predicting depth maps.
* Improved safety in navigation and obstacle detection for vehicles.

'''Commercial Applications:'''
Enhancing the accuracy of vehicle driving assistance systems can have significant commercial applications in the automotive industry, particularly in the development of autonomous vehicles.

'''Questions about Vehicle Driving Assistance Systems:'''
1. How does image processing improve the accuracy of vehicle driving assistance systems?
2. What are the potential implications of using predicted depth maps in autonomous vehicles?

'''Frequently Updated Research:'''
Stay updated on advancements in image processing technologies for vehicle driving assistance systems to ensure the latest innovations are implemented for improved accuracy and safety.


==Original Abstract Submitted==

This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a computing device may receive a predicted depth map and a measured depth map and may determine a time difference between the predicted depth map and the measured depth map. A supervision loss term may be determined based on the time difference, such as by weighting the supervision loss term based on the time difference. The computing device may train a model based on the supervision loss term, such as a model that generated the predicted depth map. Other aspects and features are also claimed and described.



[[Category:G06T7/55]]
[[Category:G01S7/4865]]
[[Category:G01S7/4915]]
[[Category:G01S17/89]]
[[Category:G01S17/931]]
[[Category:CPC_G06T7/55]]