ML Point Cloud Clustering for VR Building Inspection
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Solution Overview
Problem
Current methods for virtual building construction inspection lack a single software workflow that efficiently integrates point cloud data with Building Information Modelling (BIM) for streamlined inspection and visualization, requiring multiple software tools and increasing complexity and cost.
Innovation Solution
A single computing platform with machine learning-based point cloud clustering algorithms processes acquired point cloud data to identify building planes, cluster points, and generate 3D geometries, enabling integration with BIM models for virtual reality visualization, using modified RANSAC and DBSCAN algorithms for segmentation and clustering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple software tools are used to integrate point cloud data with BIM, then the inspection capability is improved, but the system complexity and cost increase
Solution Approach 1:
The patent combines multiple previously separate software tools into a single integrated platform that performs point cloud processing, clustering, and BIM integration. The system merges LiDAR data acquisition, point cloud clustering algorithms (DBSCAN, HDBSCAN), plane detection (RANSAC), and BIM model generation into one unified software solution, eliminating the need for multiple separate tools while maintaining comprehensive inspection capabilities
Solution Approach 2:
The inspection platform is designed as a universal system that can handle multiple inspection tasks and data types through a single interface. It processes various point cloud data formats, applies different clustering algorithms, performs plane detection, generates BIM models, and exports to multiple formats (Revit, IFC, OBJ), making it a multi-functional tool that replaces several specialized software applications
2Reliability
If multiple software tools are used for point cloud processing and BIM integration, then the inspection functionality is enhanced, but the operational complexity increases
Solution Approach 1:
The patent merges multiple operational steps and software interfaces into a single streamlined workflow. Users can perform point cloud acquisition, preprocessing, clustering, plane detection, and BIM model generation through one continuous process in a single software environment, eliminating the need to switch between multiple applications and reducing operational complexity
3Reliability
If traditional multi-software workflow is used, then comprehensive inspection is achieved, but the processing time and resources increase
Solution Approach 1:
The system performs preliminary processing of point cloud data within the same software environment before BIM integration, including downsampling, voxel grid filtering, and statistical outlier removal. These preprocessing steps are executed automatically as part of the workflow, eliminating the need to export and re-import data between software tools, thereby reducing total processing time while maintaining inspection comprehensiveness
Data Source
AI summary
The present disclosure presents system and methods for virtual building construction inspection. One such method, among others, comprises storing, by a computing device, an as-planned building information model (BIM) data for a building project, wherein the as-planned BIM data comprises a 3D design model of the building project; acquiring, by the computing device, an as-built point cloud data of the building project; processing, by the computing device, the as-built point cloud data to generate cluster points; generating, by the computing device, 3D geometry and mesh data for each cluster point set; and performing, by the computing device, virtual reality visualization of the 3D geometries generated from the as-built point cloud data and correlating the 3D geometries with the as-planned BIM data.