ML Structure Measurement Estimation Without Full 3D Modeling
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Solution Overview
Problem
Generating a full 3D model of a building to derive specific measurements is inefficient and time-consuming, necessitating a more direct method for estimating building feature measurements from digital images without creating a comprehensive 3D model.
Innovation Solution
Training a machine learning model using digital image sets and corresponding measurements to estimate specific features of structures, employing convolutional neural networks and normalization techniques to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a full 3D model is generated to derive building measurements, then measurement accuracy is improved, but time consumption and resource usage increase significantly
Solution Approach 1:
The patent extracts and isolates only the specific measurement data needed from the building, rather than generating a complete 3D model. The machine learning model is trained to directly predict specific measurements (e.g., roof area, wall length) from 2D images, extracting only the required information without creating the entire 3D structure, thus reducing time consumption while maintaining measurement accuracy.
Solution Approach 2:
Instead of performing the complete action of generating a full 3D model, the patent applies partial action by using a machine learning model to directly estimate specific measurements from 2D images. This partial approach focuses only on obtaining the needed measurement data without completing the entire 3D modeling process, significantly reducing time and resource requirements.
2Measurement precision
If a full 3D model is generated to obtain specific measurements, then measurement completeness is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent extracts only the essential measurement information directly from 2D images using a machine learning model, rather than generating a complete 3D model. This extraction approach obtains the necessary measurement data without creating the complex 3D model structure, thereby reducing device complexity and resource requirements while maintaining measurement completeness.
Solution Approach 2:
The patent replaces the mechanical 3D modeling process with a machine learning-based measurement estimation system. Instead of using complex 3D reconstruction algorithms and computational geometry operations, the system uses trained neural networks to directly predict measurements from 2D images, substituting a simpler computational approach for the complex mechanical modeling process.
3Reliability
If traditional 3D modeling methods are used to estimate building features, then measurement reliability is improved, but productivity decreases
Solution Approach 1:
The patent applies preliminary action by training the machine learning model in advance using a large dataset of 2D images with known measurements. This pre-training phase enables the model to learn accurate measurement estimation patterns, so that during actual use, reliable measurements can be obtained quickly without performing time-consuming 3D modeling operations, thus improving both reliability and productivity.
Solution Approach 2:
The patent uses copying by training the machine learning model on copies of building data (2D images with associated measurements). The model learns from these copied examples and generalizes the measurement estimation capability, enabling reliable and efficient measurement extraction without needing to perform complete 3D modeling for each new building, thereby improving productivity while maintaining reliability.
Data Source
AI summary
A computer system trains a machine learning model to estimate a real-world measurement of a feature of a structure. The machine learning model is trained using a plurality of digital image sets, wherein each image set depicts a particular structure, and a plurality of measurements, wherein each measurement is a measurement of a feature of a particular structure. After the machine learning model is trained, it is used to estimate a measurement of a feature of a particular structure depicted in a particular image set.