View source for TOYOTA RESEARCH INSTITUTE, INC. (20240249531). SYSTEMS AND METHODS FOR TARGET ASSIGNMENT FOR END-TO-END THREE-DIMENSIONAL (3D) DETECTION simplified abstract

=SYSTEMS AND METHODS FOR TARGET ASSIGNMENT FOR END-TO-END THREE-DIMENSIONAL (3D) DETECTION=


==Organization Name==

[[:Category:TOYOTA RESEARCH INSTITUTE, INC.|TOYOTA RESEARCH INSTITUTE, INC.]]


[[Category:TOYOTA RESEARCH INSTITUTE, INC.]]

==Inventor(s)==

[[:Category:DENNIS Park of Fremont CA (US)|DENNIS Park of Fremont CA (US)]][[Category:DENNIS Park of Fremont CA (US)]]

[[:Category:JIE Li of Los Altos CA (US)|JIE Li of Los Altos CA (US)]][[Category:JIE Li of Los Altos CA (US)]]

[[:Category:DIAN Chen of Los Altos CA (US)|DIAN Chen of Los Altos CA (US)]][[Category:DIAN Chen of Los Altos CA (US)]]

[[:Category:VITOR Guizilini of Santa Clara CA (US)|VITOR Guizilini of Santa Clara CA (US)]][[Category:VITOR Guizilini of Santa Clara CA (US)]]

[[:Category:ADRIEN D. Gaidon of Los Altos CA (US)|ADRIEN D. Gaidon of Los Altos CA (US)]][[Category:ADRIEN D. Gaidon of Los Altos CA (US)]]

==SYSTEMS AND METHODS FOR TARGET ASSIGNMENT FOR END-TO-END THREE-DIMENSIONAL (3D) DETECTION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240249531 titled 'SYSTEMS AND METHODS FOR TARGET ASSIGNMENT FOR END-TO-END THREE-DIMENSIONAL (3D) DETECTION

'''Simplified Explanation:'''
The patent application describes systems and methods for improved 3D object detection from images, particularly in the context of autonomous vehicles using monocular camera images. The technology predicts 3D bounding boxes and dense depth to detect objects accurately in a 3D space.

* Predicts 3D bounding boxes and dense depth for accurate object detection in a 3D space.
* Utilizes target assignments to locate objects within images of the surrounding environment.
* Enhances object detection accuracy in comparison to previous methods.
* Simplifies the end-to-end functionality for optimized performance.
* Enables autonomous operations based on the detected objects in the 3D space.

'''Potential Applications:'''
1. Autonomous vehicles for improved safety and navigation.
2. Robotics for enhanced object recognition and interaction.
3. Surveillance systems for efficient monitoring and security.
4. Augmented reality applications for realistic object placement.
5. Industrial automation for precise object detection in complex environments.

'''Problems Solved:'''
1. Enhances the accuracy of 3D object detection from images.
2. Improves the efficiency of detecting objects in a 3D space.
3. Simplifies the process of predicting 3D bounding boxes and dense depth.
4. Enables autonomous operations based on detected objects.
5. Addresses the need for advanced object detection technology in various industries.

'''Benefits:'''
1. Increased accuracy in 3D object detection.
2. Enhanced safety and navigation for autonomous vehicles.
3. Improved efficiency in object recognition tasks.
4. Simplified end-to-end functionality for optimized performance.
5. Enables real-time decision-making based on detected objects.

'''Commercial Applications:'''
The technology can be utilized in the automotive industry for autonomous vehicles, in the robotics sector for advanced object recognition, in surveillance systems for enhanced security, in augmented reality applications for realistic object placement, and in industrial automation for precise object detection in complex environments.

'''Questions about 3D Object Detection:'''
1. How does the technology predict 3D bounding boxes and dense depth for object detection?
2. What are the potential applications of improved 3D object detection technology?

'''Frequently Updated Research:'''
Researchers are continually exploring ways to enhance the accuracy and efficiency of 3D object detection algorithms, particularly in the context of autonomous systems and computer vision applications. Stay updated on the latest advancements in this field to leverage the benefits of cutting-edge technology.


==Original Abstract Submitted==

systems and methods for enhanced end-to-end three-dimensional (3-d) object detection are disclosed that improve detecting objects in the 3d space from images, such as monocular camera image that may be captured during the operation the autonomous vehicles. for example, a vehicle can include a processor device detecting one or more objects in a 3d space by predicting 3d bounding boxes and predicting dense depth associated with target assignments. the target assignments correspond to the location of objects within an image of the 3d space of a surrounding environment for the vehicle. the vehicle can also include a controller device that receives the detection of the objects in the 3d space from the processor device and performs autonomous operations. the end-to-end 3d object detection techniques achieve a high level of object detection accuracy, with significant improvements compared to previous methods, due to the simplicity and optimization of its end-to-end functionality.



[[Category:G06V20/58]]
[[Category:B60W60/00]]
[[Category:CPC_G06V20/58]]