18133173. METHOD AND SYSTEM FOR CLUSTERING OF POINT CLOUD DATA simplified abstract (HYUNDAI MOTOR COMPANY)
Contents
- 1 METHOD AND SYSTEM FOR CLUSTERING OF POINT CLOUD DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND SYSTEM FOR CLUSTERING OF POINT CLOUD DATA - 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
METHOD AND SYSTEM FOR CLUSTERING OF POINT CLOUD DATA
Organization Name
Inventor(s)
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 first identifying the class of each point data based on semantic segmentation processing, then storing the data in virtual layers associated with specific classes, and finally clustering the data within each virtual layer.
- Semantic segmentation processing is used to assign classes to each point data in the point cloud.
- The point data is stored in virtual layers based on the assigned class for each point.
- Clustering is performed on 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 environmental monitoring.
Problems Solved
This method helps in efficiently organizing and clustering large amounts of point cloud data, making it easier to analyze and extract meaningful information from the data.
Benefits
The benefits of this technology include improved data organization, better data analysis capabilities, and enhanced decision-making processes based on the clustered data.
Potential Commercial Applications
Potential commercial applications of this technology include software development for point cloud processing, data analytics services, and integration into existing systems for improved data management.
Possible Prior Art
One possible prior art for this technology could be the use of semantic segmentation in image processing to classify objects before clustering them.
What are the specific industries that could benefit from this technology?
Industries such as urban planning, construction, agriculture, and archaeology could benefit from this technology by efficiently analyzing and clustering point cloud data for various applications.
How does this technology compare to traditional methods of clustering point cloud data?
This technology offers a more structured approach to clustering point cloud data by first assigning classes based on semantic segmentation, which can lead to more accurate and meaningful clustering results compared to traditional methods.
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.
