Fit Rooms Point-Cloud Reconstruction for Structured 3D Models
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Solution Overview
Problem
Existing methods for reconstructing indoor environments from 3D point clouds struggle to reliably separate structural elements like rooms, walls, and doors from clutter and noise, leading to unsatisfactory results in high-level structured models.
Innovation Solution
A method that extracts initial planar primitives from a point cloud, extends them into infinite planes, delineates polygonal volumes, classifies these volumes based on point coverage and orientation, and concatenates them to form a piecewise planar object representing the building interior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard dense surface reconstruction approaches are applied to capture complete and detailed 3D models, then measurement completeness and resolution are improved, but the ability to separate structural elements from clutter deteriorates
Solution Approach 1:
The patent segments the point cloud data into multiple planar primitives representing different surfaces, then groups these primitives into semantic categories (walls, floors, ceilings, clutter) based on their geometric and spatial relationships. This segmentation enables separate processing and classification of structural elements versus clutter, resolving the contradiction between maintaining detail and achieving reliable separation.
Solution Approach 2:
The patent extracts planar primitives from the dense point cloud data and separates them into distinct categories. By extracting only the relevant structural planar surfaces and excluding clutter elements through classification rules, the method achieves reliable structural element separation while preserving the necessary geometric details for accurate 3D reconstruction.
2Ease of manufacture
If simplified high-level structured models are generated to optimize application characteristics, then ease of modeling is improved, but measurement accuracy and detail fidelity deteriorate
Solution Approach 1:
The patent applies different levels of modeling detail to different regions based on their semantic classification. Structural elements (walls, floors, ceilings) are reconstructed with high geometric precision using planar primitive fitting, while clutter regions are represented with simplified geometric primitives. This local quality approach enables ease of modeling for non-critical areas while maintaining measurement precision for important structural components.
3Productivity
If automated reconstruction methods are used to process large datasets efficiently, then productivity is improved, but reliability in separating permanent components from clutter deteriorates
Solution Approach 1:
The patent performs preliminary classification of point cloud data into potential structural surfaces and clutter before the main reconstruction process. By pre-identifying candidate planar primitives and filtering them based on geometric criteria (flatness, size, orientation) and spatial relationships, the method prepares the data in advance for reliable separation. This preliminary action enables automated processing to maintain both efficiency and reliability in component separation.
Data Source
AI summary
A method to reconstruct and transform a pre-existing architectural shape into a digital 3D model that can be used and processed in a computer aided design or CAD system. The method uses a point cloud acquired from the architectural shape that comprises point normal information to reconstruct a piecewise planar object that represents the architectural shape.