17455371. POINT CLOUD DATA MANAGEMENT USING KEY VALUE PAIRS FOR CLASS BASED RASTERIZED LAYERS simplified abstract (International Business Machines Corporation)

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POINT CLOUD DATA MANAGEMENT USING KEY VALUE PAIRS FOR CLASS BASED RASTERIZED LAYERS

Organization Name

International Business Machines Corporation

Inventor(s)

Hendrik F. Hamann of Yorktown Heights NY (US)

Carlo Siebenschuh of White Plains NY (US)

Siyuan Lu of Belmont CA (US)

Conrad M. Albrecht of White Plains NY (US)

POINT CLOUD DATA MANAGEMENT USING KEY VALUE PAIRS FOR CLASS BASED RASTERIZED LAYERS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17455371 titled 'POINT CLOUD DATA MANAGEMENT USING KEY VALUE PAIRS FOR CLASS BASED RASTERIZED LAYERS

Simplified Explanation

The patent application describes a method for rasterizing point cloud data using multiple processor units. Here is a simplified explanation of the abstract:

  • The method involves converting point cloud data into rasterized layers based on different classes.
  • Each rasterized layer represents a specific class within the point cloud data.
  • The processor units are responsible for performing the rasterization process.
  • Key value pairs are generated from the rasterized layers.
  • These key value pairs are then stored in a key value store.
  • The patent also covers a computer system and a computer program product for implementing this method.

Potential Applications:

  • This technology can be used in various fields that deal with point cloud data, such as 3D modeling, computer graphics, and virtual reality.
  • It can be applied in autonomous vehicles for object detection and recognition based on point cloud data.
  • The method can be utilized in environmental monitoring to analyze and visualize data collected from LiDAR sensors.

Problems Solved:

  • Rasterizing point cloud data can be a complex and computationally intensive task.
  • This method solves the problem of efficiently converting point cloud data into rasterized layers based on classes.
  • It provides a scalable solution by utilizing multiple processor units for parallel processing.

Benefits:

  • The method allows for faster and more efficient rasterization of point cloud data.
  • It enables the storage and retrieval of key value pairs, providing a structured representation of the rasterized layers.
  • The technology can handle large volumes of point cloud data, making it suitable for real-time applications.
  • It offers flexibility by allowing customization of classes and rasterization parameters.


Original Abstract Submitted

A computer implemented method rasterizes point cloud data. A number of processor units rasterizes the point cloud data into rasterized layers based on classes in which each rasterized layer in the rasterized layers corresponds to a class in the classes. The number of processor units creates key value pairs from the rasterized layers. The number of processor units store the key value pairs in a key value store. According to other illustrative embodiments, a computer system and a computer program product for rasterizing point cloud data are provided.

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