Point Cloud Attribute Assignment Using Image-Based Object Regions
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Solution Overview
Problem
Existing point cloud data processing methods rely on two-dimensional geometric shapes of objects, which may not be available or accurate when the object's form has changed, leading to inefficiencies in data grouping and attribute assignment.
Innovation Solution
Acquire region and attribute information from image data to select and assign attributes to point cloud data, enabling efficient grouping without relying on pre-existing two-dimensional geometric shapes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is acquired at high definition to improve image quality, then image quality is improved, but data amount increases and processing load increases
Solution Approach 1:
The patent divides the point cloud data processing into multiple stages: acquisition, classification, and selective processing. By segmenting the data flow and applying different processing strategies to different portions of data based on their characteristics and importance, the system maintains high definition quality where needed while reducing overall data volume and processing requirements.
2Measurement precision
If point cloud data is acquired at high definition to improve image quality, then image quality is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary classification of point cloud data immediately after acquisition, categorizing points by their semantic meaning and importance. This preliminary action enables subsequent processing steps to focus computational resources on critical data regions, thereby maintaining high image quality while significantly reducing overall processing time through selective optimization.
3Measurement precision
If detailed processing is applied to all point cloud data to improve processing accuracy, then processing accuracy is improved, but device complexity increases
Solution Approach 1:
The patent implements local quality processing by applying different processing accuracies and methods to different regions of the point cloud data based on their semantic classification. Critical regions receive detailed processing for high accuracy, while less important regions use simplified processing methods. This approach maintains overall processing accuracy while significantly reducing system complexity and computational requirements.
Data Source
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AI summary
There are provided a point cloud data processing apparatus, a point cloud data processing method, and a program for enabling easy and efficient grouping of point cloud data without using a two-dimensional geometric shape of the target object. A point cloud data processing apparatus 11 includes: an image data acquisition unit 13 that acquires image data of an object; a point cloud data acquisition unit 15 that acquires point cloud data; a recognition unit 23 that recognizes the object on the basis of the image data, and acquires a region of the object and attribute information for identifying the object; and an attribute assigning unit 25 that selects, from the point cloud data, point cloud data that belongs to the region of the object, and assigns the identified attribute information to the selected point cloud data.