Lamp Chain 3D Modeling Using Conical Unfolding Geometry
Find Innovative SolutionsGenerate Solutions
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 spatial 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 computer apparatus with a central processing unit and memory to execute the method.
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 may be improved, but recognition efficiency deteriorates and implementation cost increases
Solution Approach 1:
The patent uses a conical model as a simplified copy representation of the carrier object, avoiding the need for complex deep learning models. The conical model captures the essential geometric characteristics needed for lamp chain positioning while significantly reducing computational complexity, thus improving recognition efficiency without sacrificing necessary precision.
Solution Approach 2:
The patent transforms the complex image processing problem into a geometric parameter matching problem. By comparing lamp chain positions against the conical model's geometric parameters (radius, height, spiral pitch), the system achieves efficient and accurate positioning without requiring computationally intensive deep learning algorithms.
2Measurement precision
If complex image processing and deep learning models are used to construct lamp chain circling models, then recognition precision may be improved, but device complexity and implementation cost increase
Solution Approach 1:
The patent replaces complex deep learning models with a simple conical geometric model that captures the essential features of the carrier. This copying approach maintains sufficient recognition precision while dramatically reducing device complexity and implementation cost.
Solution Approach 2:
The conical model serves as a lightweight, computationally inexpensive representation that can be quickly constructed and processed. Unlike expensive deep learning models requiring significant computational resources, the conical model provides an efficient, low-cost solution for lamp chain positioning.
3Ease of manufacture
If traditional image processing methods are used, then implementation cost may be lower, but manufacturing precision and model accuracy deteriorate
Solution Approach 1:
The patent transforms traditional image processing into geometric parameter extraction and matching. By focusing on key geometric parameters of the conical model and lamp chain positions, the system achieves high model accuracy through simple, low-cost computational methods rather than expensive complex processing.
Solution Approach 2:
The patent discards unnecessary complex processing steps from traditional image processing while recovering and emphasizing the essential geometric parameter extraction. This selective approach maintains model accuracy by focusing on what truly matters (geometric relationships) while eliminating wasteful computational overhead.
Data Source
AI summary
A method for constructing a lamp chain circling model 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; 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, and obtaining the lamp chain circling model.