3D Data Segmentation for Architectural Model Interpretation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Digital 3D models generated from architectural scans often contain large amounts of unprocessed data, making it difficult to accurately interpret and modify them.
Innovation Solution
A system that includes an identification component to segment and modify 3D data, a data generation component to fill in missing data, and a modification component to enhance geometry and texture data, facilitating the generation and modification of 3D models and floorplans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If 3D models are generated from architectural scans, then the model contains detailed geometric information, but the data becomes difficult to interpret and modify due to large amounts of unprocessed data
Solution Approach 1:
The patent segments the unprocessed 3D data into distinct geometric primitives (walls, floors, ceilings, doors, windows) by identifying planar surfaces and their relationships. This segmentation transforms the raw point cloud into structured components that are easier to interpret and modify individually.
Solution Approach 2:
The patent extracts meaningful semantic information from the unprocessed 3D scan data by identifying and separating geometric primitives. The system extracts wall locations, floor plans, and architectural features from the raw data, removing the complexity of unprocessed points while retaining essential structural information.
2Productivity
If automatic generation of 3D models is implemented, then productivity increases, but handling missing data and ensuring accuracy becomes more difficult
Solution Approach 1:
The patent performs preliminary actions by automatically detecting and classifying geometric primitives during the initial processing stage. The system pre-identifies walls, floors, and ceilings before final model assembly, ensuring that missing data can be addressed systematically and accuracy is maintained through structured preprocessing.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously refines its identification of geometric primitives by analyzing relationships between detected surfaces. The model generation process uses feedback from detected features to correct and improve accuracy, ensuring reliable reconstruction even when data is incomplete.
Data Source
AI summary
Systems and techniques for processing three-dimensional (3D) data are presented. Captured three-dimensional (3D) data associated with a 3D model of an architectural environment is received and at least a portion of the captured 3D data associated with a flat surface is identified. Furthermore, missing data associated with the portion of the captured 3D data is identified and additional 3D data for the missing data is generated based on other data associated with the portion of the captured 3D data.