18156958. SYSTEMS AND METHODS FOR DEPTH SYNTHESIS WITH TRANSFORMER ARCHITECTURES simplified abstract (TOYOTA RESEARCH INSTITUTE, INC.)

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 18156958 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 depth synthesis, for autonomous vehicles. It introduces a Geometric Scene Representation (GSR) architecture that can synthesize depth views from various 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.
• It can predict depth maps from unseen locations by synthesizing depth views at multiple viewpoints.
• The system includes a processor device for synthesizing depth views and a controller device for performing autonomous operations based on the analysis of these depth views.

Potential Applications

The technology can be applied in various computer vision applications for autonomous vehicles, such as predicting depth maps from unseen locations, enabling advanced functions like depth interpolation and extrapolation, and enhancing the overall perception capabilities of the vehicle.

Problems Solved

The technology addresses the need for accurate depth perception in autonomous vehicles, allowing them to navigate complex environments more effectively and safely. It also solves the challenge of predicting depth maps from locations that are not directly visible to the vehicle.

Benefits

- Improved accuracy and reliability of depth perception for autonomous vehicles - Enhanced safety and efficiency in navigating complex environments - Advanced functions like depth interpolation and extrapolation for better decision-making capabilities

Commercial Applications

Title: Enhanced Computer Vision for Autonomous Vehicles This technology can be utilized in the development of autonomous vehicles for various industries, including transportation, logistics, and delivery services. It can improve the overall performance and safety of autonomous vehicles, leading to increased adoption and market growth.

Prior Art

Readers can explore prior research on computer vision systems for autonomous vehicles, depth synthesis techniques, and advanced perception algorithms to gain a deeper understanding of the existing technology landscape in this field.

Frequently Updated Research

Researchers are continuously working on improving depth synthesis algorithms, enhancing the accuracy and efficiency of computer vision systems for autonomous vehicles. Stay updated on the latest advancements in this area to leverage cutting-edge technology for autonomous vehicle development.

Questions about Enhanced Computer Vision for Autonomous Vehicles

How does the Geometric Scene Representation (GSR) architecture improve depth perception in autonomous vehicles?

The GSR architecture enables the synthesis of depth views from multiple viewpoints, allowing for advanced functions like depth interpolation and extrapolation, which enhance the accuracy and reliability of depth perception in autonomous vehicles.

What are the potential applications of depth synthesis technology in autonomous vehicles beyond predicting depth maps?

In addition to predicting depth maps from unseen locations, depth synthesis technology can be used for functions like obstacle detection, path planning, and object recognition, improving the overall perception capabilities of autonomous vehicles.

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.