Point Cloud Quality Prediction from Passive Imagery
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Solution Overview
Problem
Point cloud generation from passive imagery is a computationally expensive and time-consuming process, often resulting in wasted resources due to a lack of objective decision-making on the sufficiency of imagery quality and quantity.
Innovation Solution
A point cloud generating system that predicts the quality of passive imagery-derived point clouds using metadata-based and correlator-based scoring systems, allowing for intelligent resource allocation and improved data collection strategies, enabling objective decision-making on initiating point cloud generation and optimizing image selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If point cloud generation is performed from passive imagery, then three-dimensional models can be created, but the process becomes computationally expensive and time consuming
Solution Approach 1:
The patent applies preliminary action by performing performance prediction and quality assessment before actual point cloud generation. The system evaluates imagery quality metrics and predicts point cloud quality in advance, allowing operators to decide whether to proceed with generation, thereby avoiding wasted computational time on insufficient imagery
Solution Approach 2:
The patent implements partial action by performing quality assessment on a subset of imagery or using simplified metrics initially. The system can perform preliminary evaluation with partial processing and only fully generate point clouds for imagery that meets quality thresholds, reducing overall processing time
2Manufacturing precision
If point cloud generation is performed from passive imagery, then three-dimensional models can be created, but computational resources are wasted due to lack of objective decision-making
Solution Approach 1:
The patent implements feedback by using performance prediction results to guide point cloud generation decisions. The system provides objective quality metrics and predictions that feed back into the decision-making process, allowing operators to adjust imagery selection and processing parameters to optimize resource utilization
Solution Approach 2:
The patent applies self-service by enabling the system to automatically assess imagery quality and predict point cloud performance without manual intervention. The automated quality assessment and performance prediction systems serve themselves by making objective decisions about which imagery to process, reducing wasted computational resources
3Productivity
If objective assessment of imagery sufficiency is implemented, then resource allocation is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the quality assessment system into separate modules: performance prediction, quality assessment, and resource allocation. Each module handles specific tasks independently, making the overall complex system more manageable and maintainable while improving productivity
Data Source
AI summary
A system and method of generating point clouds from passive images. Image clusters are formed, wherein each image cluster includes two or more passive images selected from a set of passive images. Quality of the point cloud that could be generated from each image cluster is predicted for each image cluster based on a performance prediction score for each image cluster. A subset of image clusters is selected for further processing based on their performance prediction scores. A mission-specific quality score is determined for each point cloud generated and the point cloud with the highest quality score is selected for storage.