PHOTOMETRIC MASKS FOR SELF-SUPERVISED DEPTH LEARNING

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Inventor(s)

PHOTOMETRIC MASKS FOR SELF-SUPERVISED DEPTH LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18091872 titled 'PHOTOMETRIC MASKS FOR SELF-SUPERVISED DEPTH LEARNING

The abstract of this patent application describes a method for estimating the depth of an environment using a cross-attention model trained with a photometric loss associated with a single-frame depth estimation model.

The method involves generating a cross-attention cost volume based on a current image and a previous image of the environment.
It also includes generating a depth estimate of the current image using the cross-attention cost volume.
The depth estimate is used to control the action of a vehicle in the environment.

Potential Applications: - Autonomous driving systems - Robotics - Augmented reality

Problems Solved: - Accurate depth estimation in dynamic environments - Real-time decision-making based on depth information

Benefits: - Improved safety in autonomous vehicles - Enhanced navigation capabilities for robots - Immersive augmented reality experiences

Commercial Applications: Title: "Advanced Depth Estimation Technology for Autonomous Vehicles and Robotics" This technology can be used in the development of autonomous vehicles, robotic systems, and augmented reality applications. It has the potential to revolutionize the way these technologies perceive and interact with their environments.

Questions about Depth Estimation Technology: 1. How does this technology improve the accuracy of depth estimation in comparison to traditional methods? - The use of a cross-attention model trained with a photometric loss allows for more robust depth estimation by considering both current and previous images in the environment. 2. What are the key advantages of using a cross-attention model for depth estimation? - The cross-attention model can capture complex spatial relationships between different parts of the environment, leading to more accurate depth estimates.

Original Abstract Submitted

A method estimating a depth of an environment includes generating, via a cross-attention model, a cross-attention cost volume based on a current image of the environment and a previous image of the environment in a sequence of images. The method also includes generating, via the cross-attention model, a depth estimate of the current image based on the cross-attention cost volume, the cross-attention model having been trained using a photometric loss associated with a single-frame depth estimation model. The method further includes controlling an action of the vehicle based on the depth estimate.