Microscopic Virtual Learning Resource Generation
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Solution Overview
Problem
Current methods struggle to generate high-precision virtual learning resources for microscopic specimens due to limitations in 3D laser scanning technology, which fail to accurately capture and reconstruct micron-scale or nano-scale surface textures, and lack effective annotation and interactive 3D surface structure construction.
Innovation Solution
A method combining ultra-depth-of-field 3D microscopy and macro photography for framing and continuous image acquisition, followed by image registration, point cloud generation, noise removal, and 3D Delaunay algorithm-based surface modeling, along with interactive display modes and annotation schemes, to create a high-precision 3D surface model with image texture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D laser scanning technology is used to reconstruct 3D models, then large specimens can be captured, but microscopic specimens cannot be accurately reconstructed due to accuracy limits
Solution Approach 1:
The patent segments the imaging task by using ultra-depth-of-field microscopy for microscopic regions and macro photography for larger regions, then integrates them through coordinate transformation. This allows accurate capture of both microscopic and macroscopic features without being limited by a single imaging method's size constraints.
Solution Approach 2:
The patent introduces an intermediary coordinate transformation system that bridges the ultra-depth-of-field microscopy data and macro photography data. This mediator enables seamless integration of data from different scales, allowing the system to handle specimens across a wide size range while maintaining accuracy at all scales.
2Measurement precision
If ultra-depth 3D microscope is used to distinguish surface texture, then microscopic details are captured, but the rasterized modeling result cannot support interactive 3D structure construction
Solution Approach 1:
The patent replaces the traditional rasterized modeling approach with a point cloud-based 3D reconstruction method. This substitution enables the system to maintain high surface texture resolution from ultra-depth-of-field microscopy while also supporting interactive 3D structure construction, annotations, and multi-angle viewing capabilities that rasterized models lack.
3Measurement precision
If multiple imaging methods are combined to achieve high precision, then measurement accuracy improves, but system complexity increases
Solution Approach 1:
The patent creates a unified processing framework that handles both ultra-depth-of-field microscopy data and macro photography data through the same coordinate transformation and integration pipeline. This multi-functional approach allows the system to process different types of imaging data with a single set of algorithms, reducing operational complexity despite using multiple imaging methods.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the generation of high-resolution, interactive, and dynamically displayed microscopic virtual learning resources, allowing for detailed exploration and understanding of microscopic morphology and structure, enhancing VR teaching scenarios.
Implementation Method 1
adopting a combination of ultra-depth-of-field three-dimensional (3D) microscopy and macro photography to realize framing and continuous image acquisition of a specimen
Implementation Method 2
photographing the specimen from multiple angles according to requirements of overlapping photography
Implementation Method 3
realize registration and stitching of images based on an overlapping area to obtain a panoramic image of the surface of the specimen
Implementation Method 4
generating point cloud data of the specimen based on 3D image construction
Implementation Method 5
constructing a triangular surface model of the point cloud by adopting a 3D Delaunay algorithm
Data Source
AI summary
A method for generating a high-precision and microscopic virtual learning resource includes acquisition of high-definition specimen images, generation of a 3D model of a surface a specimen and interactive display of a microscopic virtual learning resource.