Optical Data Processing for 3D Model Integration
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Solution Overview
Problem
Current techniques for processing three-dimensional data from different viewpoints face challenges such as occlusion, high computational burden, and inefficiency in specifying correspondence relationships between optical data sets, particularly when dealing with large datasets like point cloud position data from laser scanners and stereophotographic images.
Innovation Solution
An optical data processing device and method that extracts and compares three-dimensional edges in specific directions between two models, calculating similarity based on edge lengths and angles, to efficiently establish correspondence relationships and integrate models, thereby overcoming occlusion and reducing data handling burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple targets are adhered to the object to be measured for positioning, then the correspondence relationship between point cloud position data and photographic image can be clearly established, but it becomes impossible to simply adhere targets when the object is a tall building or other large-scale structures
Solution Approach 1:
The invention extracts and removes the requirement for physical targets from the positioning process. Instead of adhering targets to the object, the system uses automatic feature point extraction and matching algorithms to identify corresponding points between point cloud data and photographic images, thereby solving the problem of inability to attach targets to large-scale objects like tall buildings
Solution Approach 2:
The system enables self-positioning by automatically extracting feature points from both the point cloud position data and photographic image data, and autonomously determining correspondence relationships through algorithmic matching without requiring external target markers or manual intervention for positioning
2Adaptability or versatility
If software-based matching is used to establish correspondence between point cloud data and photographic images, then no physical targets are needed, but the matching error increases, processing time becomes too long, and a large burden is applied to the calculating device
Solution Approach 1:
The invention segments the matching process into distinct stages: first extracting key feature points from point cloud data, then extracting corresponding feature points from photographic images, and finally matching these extracted feature points. This segmentation reduces the computational complexity compared to processing all data points simultaneously, thereby improving both precision and efficiency
Solution Approach 2:
The system applies different processing qualities to different parts of the data: high-precision feature point extraction is applied to key structural points, while automated matching algorithms handle the correspondence determination. This localized approach to quality control maintains high precision for critical matching while reducing overall computational burden
3Reliability
If all point cloud position data is processed to establish correspondence relationships, then complete three-dimensional modeling is achieved, but the computational burden increases significantly due to the large volume of data
Solution Approach 1:
The invention extracts only the essential feature points from the comprehensive point cloud position data that are necessary for establishing correspondence relationships with photographic images. By selecting and processing only these critical feature points rather than all data points, the system maintains complete and reliable three-dimensional modeling while significantly reducing computational complexity and processing requirements
Data Source
AI summary
A processing for specifying a correspondence relationship of feature points between two sets of optical data can be highly precise and efficiently carried out. The correspondence relationship of the perpendicular edges is obtained based on the assumption that the object is a building, in the processing for integrating the three-dimensional model obtained from the point cloud position data and the three-dimensional model obtained from the stereophotographic image. In this case, one perpendicular edge is defined by the relative position relationship with the other perpendicular edge, and the correspondence relationship is high-precisely and rapidly searched.