Scale Error Correction in Multi-Dimensional Building Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing 3D map technologies face challenges with limited texture resolution, geometry quality, inaccurate scaling, and high costs, making them difficult to update and provide real-time image data analytics for consumer and commercial use cases.
Innovation Solution
A system and method for correcting scale errors in geo-referenced multi-dimensional models by identifying known architectural dimensions such as siding rows, doors, and brick layouts in street-level images, using image processing techniques to extrapolate accurate dimensions and reconstruct the models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D mapping technologies are used to create geo-referenced models, then the models can be generated with basic 3D structure, but the scale accuracy and dimension precision are poor
Solution Approach 1:
The patent introduces an intermediary scaling correction system that uses known architectural dimensions (such as standard door heights, window dimensions, or building code specifications) as reference standards. This intermediary reference system mediates between the raw 3D model data and the final accurate model, allowing scale correction without requiring complete redesign of the mapping system.
Solution Approach 2:
The patent replaces traditional mechanical surveying and measurement systems with an automated computational approach. Instead of using physical measurement tools and manual scaling adjustments, the system uses image processing algorithms and computational geometry to automatically detect architectural features and correct scale errors through mathematical transformations.
2Manufacturing precision
If high-resolution texture and geometry are applied to improve model quality, then the visual accuracy improves, but the cost and time for updating increases
Solution Approach 1:
The patent implements a self-service system where the 3D model automatically identifies and corrects its own scaling errors using embedded architectural knowledge. The system performs self-validation by checking whether detected features conform to known architectural standards, and automatically adjusts its own geometry without requiring external manual intervention for each update.
Solution Approach 2:
The patent incorporates architectural dimension standards and building code specifications into the model creation process in advance. By pre-loading known dimensional constraints (such as standard door widths, window heights, or floor-to-ceiling distances), the system prepares correction criteria beforehand, enabling rapid automated updates when new imagery is processed.
3Measurement precision
If manual updating methods are used to maintain model accuracy, then the precision can be maintained, but the time consumption and operational difficulty increase
Solution Approach 1:
The patent transforms the update process from manual parameter adjustment to automated parameter transformation. Instead of manually measuring and adjusting each dimension, the system applies mathematical transformation parameters (scaling factors, translation vectors, rotation matrices) that are computationally derived from comparing detected architectural features against known standards, enabling rapid bulk updates across entire model datasets.
4Adaptability or versatility
If traditional mapping systems are used without architectural standards, then the implementation is simpler, but the real-time data analytics capability is limited
Solution Approach 1:
The patent creates a universal system that serves multiple functions: it not only corrects scale errors but also enables various analytics applications (building material estimation, construction planning, urban analysis). By integrating architectural knowledge graphs and standardized dimension libraries, the system becomes adaptable to different analytics needs without requiring separate specialized systems for each application.
Data Source
AI summary
A system and method is provided for identifying error and rescaling and constructing or reconstructing a multi-dimensional (e.g., 3D building) model using known architectural dimensions. The system identifies architectural elements that have known architectural standard dimensions. Dimensional measurements of architectural elements in the multi-dimensional model (poorly scaled) are compared with known architectural standard dimensions to rescale and construct/reconstruct an accurate multi-dimensional building model.