Lamp Chain Circling Modeling Using Conical Unfolding
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Solution Overview
Problem
Traditional methods for constructing lamp chain circling models are inefficient, imprecise, and costly due to reliance on complex image processing and deep learning models, making it difficult to quickly and accurately provide data for lighting design and increasing implementation costs.
Innovation Solution
A method and device for constructing a lamp chain circling model involving image analysis to determine planar and stereoscopic positions of lamp beads, unfolding a conical model into a sector-shaped reference region, and mapping these positions to a three-dimensional modeling space, using a central processing unit and memory to execute these steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex image processing and deep learning models are used to construct lamp chain circling models, then recognition precision can be improved, but computing resource consumption increases and implementation cost becomes high
Solution Approach 1:
The patent segments the lamp chain recognition task into distinct processing stages: image acquisition, coordinate transformation, and model construction. Each stage handles specific aspects of the problem independently, avoiding the need for comprehensive deep learning models while maintaining recognition precision through targeted geometric transformations and calculations.
Solution Approach 2:
The patent replaces complex computational deep learning models with geometric and mathematical methods. By using coordinate transformation systems and geometric relationships to map lamp bead positions, the invention substitutes heavy computational mechanics with lighter mathematical operations that achieve the same recognition precision with reduced resource consumption.
2Measurement precision
If deep learning models are used for lamp chain recognition, then recognition precision can be improved, but device complexity increases
Solution Approach 1:
The patent divides the recognition system into modular components: image acquisition module, coordinate transformation module, and model construction module. This segmentation reduces device complexity by organizing functions into independent units that can be implemented with simpler, more manageable subsystems rather than a monolithic deep learning architecture.
Solution Approach 2:
The patent substitutes complex deep learning device architecture with a streamlined system based on geometric transformations and coordinate mappings. This replacement reduces device complexity while maintaining recognition precision by using fundamental mathematical principles instead of complex neural network structures.
3Ease of manufacture
If traditional image processing methods are used to construct lamp chain models, then implementation cost can be reduced, but recognition efficiency becomes low
Solution Approach 1:
The patent performs preliminary coordinate transformations and establishes reference coordinate systems before conducting the actual lamp chain model construction. This preliminary action prepares the data in advance, enabling faster and more efficient model building while keeping implementation costs low by using straightforward geometric calculations rather than computationally intensive processing.
Solution Approach 2:
The patent changes the parameter representation of lamp bead positions by transforming coordinates between different reference systems. This parameter transformation enables efficient model construction by working with pre-transformed coordinate data, improving recognition efficiency while maintaining low implementation costs through mathematical operations rather than complex processing algorithms.
4Measurement precision
If deep learning models are used for lamp chain recognition, then recognition precision can be improved, but the design process becomes time-consuming
Solution Approach 1:
The patent establishes reference coordinate systems and performs preliminary coordinate transformations before the actual model construction process. This preliminary action prepares all necessary spatial relationships in advance, significantly reducing the time required for subsequent model building while maintaining recognition precision through accurate geometric mappings.
Solution Approach 2:
The patent replaces time-consuming deep learning inference processes with efficient geometric transformation calculations. By substituting neural network computations with direct mathematical transformations of coordinate data, the system achieves the same recognition precision much faster, reducing design process time while maintaining accuracy.
Data Source
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AI summary
The present disclosure relates to a method and a device for constructing a lamp chain circling model, as well as an apparatus and a product. The method comprises: obtaining an on-site image of a lamp chain that spirally circles around an external carrier, determining a planar spatial position of each front lamp bead of the lamp chain in an image view space, and determining a conical model of the external carrier; unfolding the conical model into a sector-shaped reference region in the image view space, so that each front lamp bead is located on the surface of the sector-shaped reference region according to the planar spatial position of each front lamp bead; arranging each circle of rear lamp beads on the surface of the sector-shaped reference region according the missing number of each circle of rear lamp beads on the lamp chain, and determining sector-shaped spatial positions of all the lamp beads in the sector-shaped reference region; mapping the sector-shaped spatial positions of the lamp beads on the surface of the sector-shaped reference region to corresponding stereoscopic spatial positions in a modeling space where the conical model is located, and obtaining the lamp chain circling model. The present application simplifies the construction process of the lamp chain circling model, improves the modeling efficiency and accuracy, and enables users to realize personalized lighting design easily.