2D-to-3D Construction Image Conversion for Real-Time Deviation Detection

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Solution Overview

Problem

Existing digital image conversion methods for constructing 3D models from 2D images are time-consuming and do not accurately reflect real-world conditions, such as cracks and deformations, and require significant computing resources and bandwidth.

Innovation Solution

A system comprising small form factor image conversion devices (SFFICDs) for real-time 2D to 3D rendering and 3D model comparison, utilizing edge computing and collaboration with a central system for efficient model storage and AI/ML analysis, enabling quick anomaly detection and immersive experiences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional BIM reconstruction services are used to convert 2D images to 3D models, then the 3D model can be generated, but the process takes 24 hours to several days and requires significant computing resources

Engineering Contradiction:

Improve3D model generation speedVSAvoidTime required for file conversion and BIM reconstruction

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces traditional mechanical/BIM-based reconstruction processes with neural radiation field (NeRF) technology, which uses neural networks to render 3D models from 2D images. This substitution enables real-time 3D model generation without requiring time-consuming traditional BIM reconstruction services, directly resolving the contradiction between model generation capability and processing time.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent changes the fundamental parameters of the reconstruction process by using neural networks instead of traditional geometric modeling algorithms. This parameter change in the underlying technology enables the system to generate 3D models instantly from 2D images, transforming the time requirement from days to real-time operation.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If traditional 2D to 3D conversion methods are used, then structural configuration can be reconstructed, but real-world conditions such as cracks and deformations cannot be accurately captured

Engineering Contradiction:

ImproveAccuracy of real-world condition detectionVSAvoidReflection of actual structural conditions

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The patent replaces traditional geometric reconstruction methods with neural radiation field technology, which learns directly from 2D images to capture real-world conditions. The neural network processes image data to generate 3D representations that include surface details like cracks and deformations, enabling accurate measurement of real-world structural conditions rather than just idealized geometric forms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If high-quality 3D rendering is performed to capture detailed real-world conditions, then measurement precision improves, but computing resources and bandwidth requirements increase significantly

Engineering Contradiction:

ImproveDetail accuracy in 3D modelVSAvoidComputing resources and bandwidth consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent substitutes traditional high-compute geometric rendering with neural network-based rendering. The neural radiation field approach processes images through neural networks that can run on standard consumer hardware, significantly reducing computing resource requirements compared to traditional high-quality 3D rendering while maintaining detailed measurement precision.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent creates a neural network representation (digital twin) of the 3D scene that can be processed and rendered efficiently. This neural copy allows the system to maintain high measurement precision through the neural network's detailed representation capabilities while requiring far fewer computing resources than traditional ray-tracing or geometric rendering methods.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20260004515A1Real time image converter using 2d to 3D rendering to provide a construction compliance system

Publication Date: 2026.01.01 DELL PROD LP

AI summary

A construction compliance system includes an image capture device located in a far edge of the construction compliance system that is proximate to a construction project, and a near edge processor located in a near edge of the construction compliance system that is remote from the construction project. The image conversion device receives an image of a construction status of a construction project, and renders a three-dimensional model of the construction status based on the image. The near edge processor receives a three-dimensional model of a construction plan of the construction project, compares the three-dimensional model of the construction status with the three-dimensional model of the construction plan to determine whether the construction status deviates from the construction plan, and provides an indication when the construction status deviates from the construction plan.