TOYOTA RESEARCH INSTITUTE, INC. (20240249465). SYSTEMS AND METHODS FOR DEPTH SYNTHESIS WITH TRANSFORMER ARCHITECTURES simplified abstract

SYSTEMS AND METHODS FOR DEPTH SYNTHESIS WITH TRANSFORMER ARCHITECTURES

Organization Name

TOYOTA RESEARCH INSTITUTE, INC.

Inventor(s)

VITOR Guizilini of Santa Clara CA (US)

Igor Vasiljevic of Pacifica CA (US)

Adrien D. Gaidon of San Jose CA (US)

Greg Shakhnarovich of Chicago IL (US)

Matthew Walter of Chicago IL (US)

Jiading Fang of Chicago IL (US)

Rares A. Ambrus of San Francisco CA (US)

SYSTEMS AND METHODS FOR DEPTH SYNTHESIS WITH TRANSFORMER ARCHITECTURES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240249465 titled 'SYSTEMS AND METHODS FOR DEPTH SYNTHESIS WITH TRANSFORMER ARCHITECTURES

Simplified Explanation: The patent application describes systems and methods for enhancing computer vision capabilities, specifically focusing on depth synthesis for autonomous vehicles. This involves using a geometric scene representation (GSR) architecture to synthesize depth views from different viewpoints, enabling functions like depth interpolation and extrapolation.

• The patent introduces a geometric scene representation (GSR) architecture for synthesizing depth views at arbitrary viewpoints.
• The GSR architecture enables advanced functions such as depth interpolation and extrapolation, which are beneficial for various computer vision applications in autonomous vehicles.
• By implementing functions like depth interpolation and extrapolation, the system can predict depth maps from unseen locations, enhancing the vehicle's perception of its surroundings.
• The vehicle equipped with this technology includes a processor device for synthesizing depth views at multiple viewpoints and a controller device for performing autonomous operations based on the analysis of these depth views.

Potential Applications: 1. Autonomous driving systems can benefit from improved depth perception for better navigation and obstacle avoidance. 2. Robotics applications could use enhanced computer vision capabilities for tasks like object recognition and manipulation. 3. Surveillance systems may utilize the technology to improve monitoring and tracking of objects in complex environments.

Problems Solved: 1. Enhances depth perception in autonomous vehicles for safer and more efficient operation. 2. Enables accurate depth mapping in challenging scenarios where direct measurements are limited. 3. Improves the overall reliability and performance of computer vision systems in various applications.

Benefits: 1. Increased safety and reliability in autonomous vehicle operations. 2. Enhanced object recognition and tracking capabilities in robotics and surveillance systems. 3. Improved efficiency and accuracy in computer vision applications.

Commercial Applications: Title: Enhanced Computer Vision for Autonomous Vehicles: Market Implications This technology has significant commercial potential in industries such as autonomous vehicles, robotics, and surveillance systems. Companies developing self-driving cars, robotic solutions, and security systems could leverage this innovation to improve their products' performance and capabilities.

Prior Art: Readers interested in exploring prior art related to this technology can start by researching advancements in computer vision, depth synthesis, and autonomous vehicle technologies. Examining academic papers, industry publications, and patent databases may provide insights into similar innovations and developments in the field.

Frequently Updated Research: Researchers are continually exploring new methods and algorithms to enhance computer vision capabilities, particularly in the context of autonomous vehicles. Stay updated on the latest advancements in depth synthesis, scene representation, and machine learning techniques to understand the evolving landscape of this technology.

Questions about Enhanced Computer Vision for Autonomous Vehicles: 1. How does the GSR architecture improve depth perception in autonomous vehicles? 2. What are the potential implications of this technology for robotics and surveillance applications?

Original Abstract Submitted

systems and methods for enhanced computer vision capabilities, particularly including depth synthesis, which may be applicable to autonomous vehicle operation are described. a vehicle may be equipped with a geometric scene representation (gsr) architecture for synthesizing depth views at arbitrary viewpoints. the gsr architecture synthesizes depth views enable advanced functions, including depth interpolation and depth extrapolation. the gsr architecture implements functions (i.e., depth interpolation, depth extrapolation) that are useful for various computer vision applications for autonomous vehicles, such as predicting depth maps from unseen locations. for example, a vehicle includes a processor device synthesizing depth views at multiple viewpoints, where the multiple viewpoints are from image data of a surrounding environment for the vehicle. further, the vehicle can have a controller device that receives depth views from the processor device and performs autonomous operations in response to analysis of the depth views.