Digital Twin Submillimeter Alignment with Multimodal 3D Deep Learning Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing mixed reality systems face challenges in rendering large, complex 3D models with high fidelity and achieving sub-10 mm accuracy in aligning digital twins with real-world objects, requiring extensive training data that is often confidential and difficult to obtain, which limits their application in critical and less critical scenarios.
Innovation Solution
A multimodal 3D deep learning fusion system that utilizes multiple pre-trained neural networks with different training datasets to reduce data complexity, enabling submillimeter alignment by generating histograms from 3D point clouds and using simpler machine learning models for precise object alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning based 3D object tracking is used to match the complexity of the data, then measurement precision is improved, but the quantity of training data required increases significantly
Solution Approach 1:
The patent segments the complex 3D alignment problem into multiple stages: initial rough alignment using simpler methods, followed by iterative refinement. This allows the system to achieve high precision without requiring deep learning models to process all data from scratch, thereby reducing training data requirements while maintaining sub-10mm accuracy.
Solution Approach 2:
The patent performs preliminary actions by pre-processing 3D point cloud data to extract key features and reduce dimensionality before applying deep learning models. This preliminary feature extraction reduces the complexity of the input data, allowing smaller models with less training data to achieve the same alignment precision.
2Measurement precision
If large complex 3D models are rendered with high fidelity, then measurement precision is improved, but computing power requirements increase
Solution Approach 1:
The patent divides large complex 3D models into smaller mesh segments that can be rendered independently. This segmentation allows the system to maintain high fidelity rendering of critical regions while using lower computational resources for less critical areas, thereby reducing overall computing power requirements.
Solution Approach 2:
The patent applies partial rendering strategies where only portions of the 3D model requiring high fidelity are rendered at full resolution, while other portions use lower resolution. This selective approach maintains measurement precision for critical measurement areas while significantly reducing the total computing power needed for rendering the complete scene.
3Measurement precision
If the alignment accuracy is improved to sub-10 mm, then measurement precision is improved, but the complexity of the system increases
Solution Approach 1:
The patent segments the alignment system into modular components: sensor modules, processing modules, and output modules. Each module performs a specific function in the alignment pipeline, making the overall complex system easier to manage, debug, and maintain while achieving sub-10mm accuracy through coordinated operation of these simplified individual components.
Data Source
AI summary
A mixed reality (MR) system and method performs alignment of a digital twin and the corresponding real-world object using 3D deep neural network structures using multimodal fusion and simplified machine learning to cluster label distributions (output of 3D deep neural network trained by generic 3D benchmark dataset) that are used to reduce the training data requirements to directly train a 3D deep neural network structures. In one embodiment, multiple 3D deep neural network structures, such as PointCNN, 3D-Bonet, RandLA, etc., may be trained by different generic 3D benchmark datasets, such as ScanNet, ShapeNet, S3DIS, inadequate 3D training dataset, etc.