Point Cloud Boundary Correction for Precise Object Feature Extraction
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Solution Overview
Problem
Existing techniques for generating information from point cloud data lack precision due to insufficient consideration of important features related to the object in the processing target area.
Innovation Solution
An image processing apparatus and method that involves acquiring point cloud data, setting partial areas with a first boundary, computing feature values for these areas, and correcting the processing target area by setting a new boundary based on areas satisfying a reference feature value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed processing target area is used in point cloud data processing, then the processing scope is clearly defined, but important features related to the object may be excluded from the processing area, reducing processing precision
Solution Approach 1:
The patent applies preliminary action by computing feature values for multiple partial areas before finalizing the processing target area boundary. The boundary correction unit预先 calculates feature values for each partial area and uses these pre-computed values to determine the optimal boundary position, ensuring important features are included without requiring complex real-time adjustments during processing.
Solution Approach 2:
The patent segments the processing area into multiple partial areas and computes feature values for each segment independently. This segmentation allows the system to evaluate different regions separately and construct the final boundary based on the aggregate feature values, improving precision while maintaining manageable processing complexity through modular computation.
2Measurement precision
If the processing target area boundary is adjusted to include more features, then processing precision improves, but the computational complexity increases
Solution Approach 1:
The patent divides the processing area into multiple partial areas and computes feature values for each segment independently and in parallel. This segmentation enables efficient computation by distributing the workload across multiple regions, reducing overall processing time while maintaining the ability to accurately identify and include important features in the final boundary.
Solution Approach 2:
The patent computes feature values for multiple partial areas that extend beyond the initial boundary estimate, performing slightly more computation than strictly necessary for the final boundary. This partial excessive action ensures that all potentially important features are evaluated, allowing the boundary correction unit to make an informed decision that maximizes information generation accuracy without requiring exhaustive processing of the entire space.
Data Source
AI summary
For maintaining precision in processing of generating information relating to an object by using point cloud data, an image processing apparatus includes a data acquisition unit, a feature value computation unit, and an area correction unit. The data acquisition unit acquires point cloud data generated by scanning a space to be measured. The feature value computation unit sets a plurality of partial areas with a first boundary of a processing target area in the point cloud data as a reference, and computes a feature value for each of the plurality of partial areas. The area correction unit corrects the processing target area by setting a new second boundary, based on a position of a partial area satisfying a reference relating to the feature value.