Point Cloud Thinning via Distance-Based Skip Ratio
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Solution Overview
Problem
Existing methods for thinning point clouds, such as random removal or tiling, often omit important details and introduce artifacts, failing to effectively balance data reduction with visual fidelity, especially when viewed from different perspectives.
Innovation Solution
A method that excludes data points from a point cloud based on their distance from the imaging device's point of view, prioritizing closer points for inclusion and gradually thinning further points, using a skipRatio formula to determine inclusion, thereby maintaining detail near the user's perspective while reducing detail further away.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If random points are removed from the point cloud, then the number of data points is reduced, but important detail may be omitted and unwanted artifacts are introduced
Solution Approach 1:
The patent applies local quality by differentiating the treatment of points based on their spatial location relative to the viewer. Points closer to the viewer's perspective are preserved with higher density while points farther away are progressively removed. This creates a non-uniform thinning pattern that adapts to the visual importance of different regions, maintaining detail where it matters most while reducing data elsewhere.
2Productivity
If the point cloud is thinned to reduce data points, then processing performance is improved, but visual detail is lost
Solution Approach 1:
The patent implements local quality by applying different thinning densities to different spatial regions. The skip ratio increases with distance from the viewer, creating a gradient where nearby points are densely retained for visual fidelity while distant points are sparsely sampled for performance. This resolves the contradiction by making the thinning strategy adaptive rather than uniform.
3Quantity of substance
If tile-based thinning is applied with fewer points in distant tiles, then data processing is reduced, but tile boundaries appear in the display and detailed data near the user may be omitted
Solution Approach 1:
The patent employs asymmetry by creating an asymmetric thinning pattern radiating from the viewer's perspective point. Rather than symmetric tile-based reduction, the thinning intensity varies continuously with distance from the perspective point, with no artificial boundaries. This asymmetric approach naturally prioritizes the foreground while smoothly transitioning to sparser representation in the background, eliminating visible tile boundaries.
4Quantity of substance
If uniform thinning is applied across the entire point cloud, then processing resources are reduced, but detail is lost uniformly including near the user's view
Solution Approach 1:
The patent directly addresses uniform thinning's flaw by implementing local quality through perspective-based differentiation. The skip ratio is calculated as a function of distance from the viewer's perspective point, creating a gradient where nearby points have low skip ratios (preserved) and distant points have high skip ratios (removed). This non-uniform approach maintains critical detail in the viewer's focal region while reducing data elsewhere.
Data Source
AI summary
A system and method for thinning a point cloud. In one aspect of the method, the point cloud is generated by an imaging device and data points are thinned out of the point cloud based upon their distance from the imaging device.