Point Cloud Simplification via Confidence-Weighted Contraction
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Solution Overview
Problem
Existing methods for simplifying point cloud data often result in loss of key features or require significant computational resources, as they either risk losing important details or are complex and costly to implement, especially when dealing with large datasets from LiDAR and laser scanning devices.
Innovation Solution
A method involving recursive spatial partitioning of points into clusters, calculating tangent plane estimates, and deriving confidence factors to identify representative points and iteratively contract point-pairs with the lowest contraction error metric, while weighting by confidence factors, to preserve high-curvature areas and reduce data size efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional simplification methods like sub-sampling decimation or uniform spatial clustering are used, then data size is reduced, but key features and details are lost
Solution Approach 1:
The patent applies local quality by using confidence factors to differentiate between various regions of the point cloud. Points in high-curvature regions (edges, corners) receive higher confidence factors and are preserved, while points in flat regions receive lower confidence factors and are more likely to be removed. This localized quality assessment resolves the contradiction by preserving important features while reducing overall data size.
Solution Approach 2:
The patent changes parameters by introducing confidence factors that dynamically weight the importance of different points based on local geometry. The confidence factor is calculated based on the deviation from the tangent plane, which varies across different regions. This parameter change enables selective preservation of features while achieving data reduction.
2Loss of information
If complex methods like k-neighbours plane reconstruction are used, then key features are preserved, but computational expense and processing time increase significantly
Solution Approach 1:
The patent segments the point cloud processing into independent point-wise operations. Instead of complex global operations like k-neighbours that require examining multiple points together, the method calculates confidence factors for each point independently based on its local geometry relative to the tangent plane. This segmentation dramatically reduces computational complexity while preserving features.
Solution Approach 2:
The patent uses simple geometric calculations (distance to tangent plane) instead of complex computational geometry operations. The confidence factor calculation is computationally inexpensive compared to k-neighbours methods, enabling fast processing while still capturing essential features through the local geometry assessment.
3Loss of time
If aggressive data reduction is applied, then processing time and storage requirements decrease, but distortion and feature loss increase
Solution Approach 1:
The patent applies local quality by assigning different confidence levels to points based on their geometric importance. Points in regions requiring high geometric accuracy (high-curvature areas) are protected from aggressive reduction, while points in regions tolerant of approximation (flat areas) are reduced more aggressively. This resolves the contradiction by maintaining precision where needed while achieving time reduction overall.
Solution Approach 2:
The patent applies partial action by selectively reducing only those points that have low confidence factors (flat regions), while leaving points with high confidence factors (feature regions) unchanged. This partial reduction strategy achieves processing time benefits without excessive distortion in critical areas.
Data Source
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AI summary
There is provided a method for simplification of a point cloud comprising a set of points. The method comprises implementing recursive spatial partitioning of the set of points into a hierarchy of clusters and, for each cluster, calculating a tangent plane estimate for the points in the cluster and deriving a confidence factor for the calculated tangent plane estimate. The method further comprises identifying representative points within each cluster in the hierarchy and, for each representative point, defining a point-pair that consists of the representative point and a representative point of an immediate parent cluster; calculating a contraction error metric for each point-pair that is weighted by the inverse of the confidence factors calculated for the clusters associated with each representative point; and iteratively contracting the point-pair with the lowest contraction error metric, updating remaining point-pairs as a result of the contraction, and revising the contraction error metric of the updated point-pairs accordingly.