View source for 18133173. METHOD AND SYSTEM FOR CLUSTERING OF POINT CLOUD DATA simplified abstract (KIA CORPORATION)

=METHOD AND SYSTEM FOR CLUSTERING OF POINT CLOUD DATA=


==Organization Name==

[[:Category:KIA CORPORATION|KIA CORPORATION]]


[[Category:KIA CORPORATION]]

==Inventor(s)==

[[:Category:Mu Gwan Jeong of Seoul (KR)|Mu Gwan Jeong of Seoul (KR)]][[Category:Mu Gwan Jeong of Seoul (KR)]]

[[:Category:Nam Gyun Kim of Seongnam-si (KR)|Nam Gyun Kim of Seongnam-si (KR)]][[Category:Nam Gyun Kim of Seongnam-si (KR)]]

==METHOD AND SYSTEM FOR CLUSTERING OF POINT CLOUD DATA - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18133173 titled 'METHOD AND SYSTEM FOR CLUSTERING OF POINT CLOUD DATA

==Simplified Explanation==

The method described in the abstract involves clustering point cloud data by identifying the class of each point data, storing them in virtual layers based on their class, and then clustering the data within each virtual layer.

* Identifying the class of each point data based on semantic segmentation processing.
* Storing point data in virtual layers based on their assigned class.
* Clustering the point data within each virtual layer.

== Potential Applications ==
This technology could be applied in various fields such as autonomous driving, robotics, 3D modeling, and augmented reality for object recognition, scene understanding, and navigation.

== Problems Solved ==
This technology helps in organizing and clustering large amounts of point cloud data efficiently, enabling better analysis and understanding of complex environments.

== Benefits ==
The method allows for more accurate and detailed clustering of point cloud data, leading to improved object recognition, classification, and overall data analysis.

== Potential Commercial Applications ==
Potential commercial applications include software development for autonomous vehicles, robotics, urban planning, and virtual reality applications.

== Possible Prior Art ==
One possible prior art could be the use of traditional clustering algorithms on point cloud data, which may not take into account the semantic information of the data for more accurate clustering. 

== Unanswered Questions ==

=== How does this method compare to existing point cloud clustering techniques? ===
The article does not provide a direct comparison to existing point cloud clustering techniques, leaving room for further analysis on the effectiveness and efficiency of this method compared to traditional approaches.

=== What are the limitations of this method in terms of scalability and real-time processing? ===
The article does not address the scalability and real-time processing capabilities of this method, which are crucial factors for practical applications in dynamic environments.


==Original Abstract Submitted==

A method for clustering point cloud data includes the following steps of identifying a class of each point data of the point cloud data, the class assigned according to a semantic segmentation processing of the point cloud data, storing a plurality of point data of the point cloud data in virtual layers based on the class assigned to each point data, the virtual layers each associated with at least one class; and clustering the plurality of point data for each of the virtual layers.



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