17903712. SELF-SUPERVISED TRAINING FROM A TEACHER NETWORK FOR COST VOLUME BASED DEPTH ESTIMATES simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)
Contents
- 1 SELF-SUPERVISED TRAINING FROM A TEACHER NETWORK FOR COST VOLUME BASED DEPTH ESTIMATES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SELF-SUPERVISED TRAINING FROM A TEACHER NETWORK FOR COST VOLUME BASED DEPTH ESTIMATES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Unanswered Questions
- 1.10 Original Abstract Submitted
SELF-SUPERVISED TRAINING FROM A TEACHER NETWORK FOR COST VOLUME BASED DEPTH ESTIMATES
Organization Name
TOYOTA JIDOSHA KABUSHIKI KAISHA
Inventor(s)
Vitor Guizilini of Santa Clara CA (US)
SELF-SUPERVISED TRAINING FROM A TEACHER NETWORK FOR COST VOLUME BASED DEPTH ESTIMATES - A simplified explanation of the abstract
This abstract first appeared for US patent application 17903712 titled 'SELF-SUPERVISED TRAINING FROM A TEACHER NETWORK FOR COST VOLUME BASED DEPTH ESTIMATES
Simplified Explanation
The abstract describes a method for controlling a vehicle in an environment using a cross-attention model to generate a cost volume based on current and previous images, combining features, generating a depth estimate, and controlling the vehicle based on the estimate.
- Method involves using a cross-attention model to generate a cost volume based on current and previous images in a sequence.
- Features are combined by merging cost volume features with single-frame features from the current image.
- Single-frame features are generated using a single-frame encoding model.
- A depth estimate of the current image is produced based on the combined features.
- The vehicle's actions are controlled based on the depth estimate.
Potential Applications
This technology could be applied in autonomous vehicles, robotics, surveillance systems, and augmented reality applications.
Problems Solved
1. Improved control and navigation of vehicles in complex environments. 2. Enhanced depth estimation for better understanding of surroundings.
Benefits
1. Increased safety and efficiency in vehicle operations. 2. Better decision-making capabilities for autonomous systems. 3. Enhanced perception and understanding of the environment.
Potential Commercial Applications
Optimizing Vehicle Control and Navigation using Cross-Attention Models
Unanswered Questions
How does this method handle dynamic environments and changing conditions?
The article does not specify how the method adapts to dynamic environments and changing conditions to ensure accurate depth estimation and control.
What computational resources are required to implement this method effectively?
The article does not provide information on the computational resources needed to implement this method effectively, which could be crucial for practical applications.
Original Abstract Submitted
A method for controlling a vehicle in 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 combined features by combining cost volume features of the cross-attention cost volume with single-frame features associated with the current image. The single-frame features may be generated via a single-frame encoding model. The method further includes generating a depth estimate of the current image based on the combined features. The method still further includes controlling an action of the vehicle based on the depth estimate.
