Qualcomm incorporated (20240135559). DEPTH ESTIMATION USING IMAGE AND SPARSE DEPTH INPUTS simplified abstract
Contents
- 1 DEPTH ESTIMATION USING IMAGE AND SPARSE DEPTH INPUTS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DEPTH ESTIMATION USING IMAGE AND SPARSE DEPTH INPUTS - 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 Possible Prior Art
- 1.10 Original Abstract Submitted
DEPTH ESTIMATION USING IMAGE AND SPARSE DEPTH INPUTS
Organization Name
Inventor(s)
Georgi Dikov of Amsterdam (NL)
Mohsen Ghafoorian of Diemen (NL)
Joris Johannes Lambertus Van Vugt of Utrecht (NL)
DEPTH ESTIMATION USING IMAGE AND SPARSE DEPTH INPUTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135559 titled 'DEPTH ESTIMATION USING IMAGE AND SPARSE DEPTH INPUTS
Simplified Explanation
The patent application describes a method for generating depth information from images using a neural network model.
- Obtaining an image of a scene and depth information associated with objects in the scene.
- Processing the image and depth information using an encoder of a neural network model to generate a feature representation.
- Processing the feature representation using a decoder of the neural network model to generate a depth output corresponding to the image.
Potential Applications
This technology could be applied in various fields such as:
- Autonomous driving for better understanding of the environment.
- Augmented reality for more realistic virtual overlays.
Problems Solved
This technology addresses the following issues:
- Accurately estimating depth information from images.
- Enhancing the quality of depth perception in computer vision applications.
Benefits
The benefits of this technology include:
- Improved accuracy in depth estimation.
- Enhanced performance in tasks requiring depth information.
Potential Commercial Applications
The potential commercial applications of this technology include:
- Development of advanced camera systems for smartphones.
- Integration into robotics for better navigation capabilities.
Possible Prior Art
One possible prior art for this technology could be the use of traditional computer vision techniques for depth estimation from images.
What are the limitations of this technology in real-world applications?
This technology may face limitations in scenarios with complex scenes or rapidly changing environments, where accurate depth estimation is challenging.
How does this technology compare to existing depth estimation methods?
This technology offers improved accuracy and efficiency compared to traditional methods, making it more suitable for real-time applications requiring depth information.
Original Abstract Submitted
systems and techniques are provided for generating depth information from one or more images. for instance, a method can include obtaining an image of a scene and obtaining depth information associated with one or more objects in the scene. the method can include processing, using an encoder of a neural network model, the image and the depth information to generate a feature representation of the image and the depth information. the method can further include processing, using a decoder of the neural network model, the feature representation of the image and the depth information to generate a depth output corresponding to the image.
