Qualcomm incorporated (20240177329). SCALING FOR DEPTH ESTIMATION simplified abstract

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SCALING FOR DEPTH ESTIMATION

Organization Name

qualcomm incorporated

Inventor(s)

Hong Cai of San Diego CA (US)

Yinhao Zhu of La Jolla CA (US)

Jisoo Jeong of San Diego CA (US)

Yunxiao Shi of San Diego CA (US)

Fatih Murat Porikli of San Diego CA (US)

SCALING FOR DEPTH ESTIMATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240177329 titled 'SCALING FOR DEPTH ESTIMATION

Simplified Explanation

The abstract describes a system and method for processing sensor data, specifically for predicting depth maps for images using machine learning and depth values obtained from a tracker.

  • The process involves determining a predicted depth map for an image using a trained machine learning system.
  • Depth values for the image are obtained from a tracker based on feature points between frames.
  • The predicted depth map is scaled using the obtained depth values to provide scale-correct depth prediction values.

Potential Applications

This technology could be applied in various fields such as autonomous vehicles, augmented reality, and robotics for accurate depth perception and object recognition.

Problems Solved

This technology solves the problem of accurately predicting depth maps for images, especially when depth values for all pixels are not available.

Benefits

The benefits of this technology include improved accuracy in depth prediction, which can enhance the performance of applications relying on depth information.

Potential Commercial Applications

Potential commercial applications of this technology include in the development of advanced driver assistance systems, virtual reality applications, and surveillance systems.

Possible Prior Art

One possible prior art could be the use of traditional computer vision techniques for depth estimation in images before the advent of machine learning-based approaches.

What are the limitations of this technology in real-world applications?

The limitations of this technology in real-world applications may include the computational resources required for processing large amounts of sensor data and the need for continuous training of the machine learning system to adapt to different environments.

How does this technology compare to existing depth prediction methods?

This technology offers the advantage of leveraging machine learning to predict depth maps, which can lead to more accurate and robust depth estimation compared to traditional methods that rely solely on feature points or geometric calculations.


Original Abstract Submitted

systems and techniques are provided for processing sensor data. for example, a process can include determining, using a trained machine learning system, a predicted depth map for an image, the predicted depth map including a respective predicted depth value for each pixel of the image. the process can further include obtaining depth values for the image, the depth values including depth values for less than all pixels of the image from a tracker configured to determine the depth values based on one or more feature points between frames. the process can further include scaling the predicted depth map for the image using and the depth values. the output of the process can be scale-correct depth prediction values.

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