Neural Radiance Representation for Fast Lightweight 3D Visualization
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Solution Overview
Problem
Current 3D visualization techniques, such as Neural Radiance Fields (NeRF), require expensive setups, retraining for each new scene, and are computationally intensive, limiting their applicability and scalability.
Innovation Solution
Utilizing a lightweight neural network model, like NeRF, to generate synthetic 3D views from limited 2D images, enabling fast and cost-effective 3D reconstruction and digital twin generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D visualization techniques are used, then high quality 3D visualization can be achieved, but computational cost and resource requirements become excessively high
Solution Approach 1:
The patent creates a simplified digital twin copy of the physical system that replicates essential 3D visualization capabilities without requiring full computational fidelity. This copy enables high-quality visualization while consuming significantly fewer computational resources than traditional methods.
Solution Approach 2:
The system transforms the complex 3D visualization problem into a lower-dimensional representation by changing key parameters from full radiance field computation to simplified geometric and radiometric parameters, reducing computational cost while maintaining visualization quality.
2Measurement precision
If Neural Radiance Fields are retrained for each new scene, then accurate scene representation can be achieved, but training time and resource requirements increase
Solution Approach 1:
The patent pre-computes and stores transformation parameters and geometric representations during system initialization or offline processing. This preliminary action enables rapid scene adaptation without requiring full retraining, reducing training time while maintaining representation accuracy.
Solution Approach 2:
The system develops a universal digital twin framework that can adapt to multiple different scenes and configurations using the same core architecture and transformation parameters, eliminating the need for scene-specific retraining while maintaining accurate representation.
3Measurement precision
If expensive computational setups are used for 3D reconstruction, then high quality results can be achieved, but scalability and portability are limited
Solution Approach 1:
The patent replaces expensive, complex computational setups with lightweight, simplified algorithms and data structures that can be rapidly deployed and discarded. This approach maintains reconstruction quality while dramatically improving scalability and portability to different platforms and applications.
Data Source
AI summary
The description is directed to a method of visualization, involving capturing a multitude of two-dimensional images, generating transforms for each of the multitude of two-dimensional images, generating a three-dimensional representation of an object or scene based on the transforms, where the three-dimensional representation is a novel view, and rendering the three-dimensional representation of the object or scene.