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

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


==Organization Name==

[[:Category:International Business Machines Corporation|International Business Machines Corporation]]


[[Category:International Business Machines Corporation]]

==Inventor(s)==

[[:Category:Hendrik F. Hamann of Yorktown Heights NY (US)|Hendrik F. Hamann of Yorktown Heights NY (US)]][[Category:Hendrik F. Hamann of Yorktown Heights NY (US)]]

[[:Category:Carlo Siebenschuh of White Plains NY (US)|Carlo Siebenschuh of White Plains NY (US)]][[Category:Carlo Siebenschuh of White Plains NY (US)]]

[[:Category:Siyuan Lu of Belmont CA (US)|Siyuan Lu of Belmont CA (US)]][[Category:Siyuan Lu of Belmont CA (US)]]

[[:Category:Conrad M. Albrecht of White Plains NY (US)|Conrad M. Albrecht of White Plains NY (US)]][[Category: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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