Point Cloud Equipment Management Without Full 3D Modeling
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Solution Overview
Problem
The use of point cloud data for facility management is hindered by enormous data volumes, requiring significant man-hours and costs for three-dimensional modeling, making it impractical for low-revenue infrastructure facilities.
Innovation Solution
A system that generates management moving images using selected point cloud data without converting it into a three-dimensional model, reducing processing costs and enhancing rendering speed while attaching nameplate data for equipment identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If point cloud data is converted into three-dimensional models for facility management, then the visualization quality and detail representation are improved, but the processing time and costs increase enormously
Solution Approach 1:
The patent extracts only the essential visual information needed for facility management from the complete point cloud data, rather than processing all data points into full 3D models. This selective extraction approach maintains adequate visualization quality while dramatically reducing processing time and computational resources.
Solution Approach 2:
The patent applies partial action by processing only a subset of point cloud data that is sufficient for management purposes. Instead of converting all point cloud data into detailed 3D models, the system processes a representative portion that provides the necessary visualization for facility management decisions.
2Measurement precision
If complete point cloud data is processed into three-dimensional models, then the detail and accuracy of facility representation are improved, but the computational resources and costs become prohibitive for low-revenue facilities
Solution Approach 1:
The system extracts only the critical geometric and spatial features from point cloud data that are necessary for facility management accuracy. This selective extraction maintains measurement precision for management purposes while avoiding the computational overhead of processing complete high-fidelity 3D models.
Solution Approach 2:
The patent applies local quality by providing different levels of data processing detail in different regions or aspects of the facility. Critical areas receive higher processing accuracy while less critical areas use simplified representations, optimizing the balance between measurement precision and computational resource usage.
3Loss of information
If all point cloud data is animated to create comprehensive management moving images, then the completeness of facility documentation is improved, but the rendering time and processing costs increase significantly
Solution Approach 1:
The patent extracts key spatial and temporal information from point cloud sequences to create representative moving images for facility management. This selective extraction maintains documentation completeness for management decisions while dramatically improving rendering speed by avoiding animation of all data points.
Solution Approach 2:
The system applies partial action by animating only a representative subset of point cloud data that captures the essential facility changes and conditions. This approach provides sufficient documentation completeness for management purposes while maintaining high rendering productivity.
Data Source
AI summary
To provide a system that can obtain moving images for equipment maintenance and management using point cloud data, with an inexpensive configuration. A program according to the present invention makes a processor of a computer execute steps including: a first step of receiving input of point cloud data that is acquired by measuring a space including a management object with a three-dimensional point cloud scanner; a second step of selecting a plurality of pieces of point data, which are part of the point cloud data that is acquired, so as to determine point cloud data to be animated; and a third step of animating the point cloud data, which is composed of the plurality of pieces of point data that are selected in the second step, so as to generate a management moving image in which the management object is represented as three-dimensional data in a virtual space.