Adaptive Point Resampling for High-Fidelity Curves and Surfaces

The patent applies local quality by varying the sampling density across different regions of the curve or surface based on local geometric properties. Regions with high curvature or significant geometric features are sampled more densely, while flat or less important regions are sampled more sparsely. This resolves the contradiction by maintaining high representation fidelity only where geometrically necessary, thereby reducing overall memory usage and processing resources.

The patent changes the sampling density parameter dynamically based on local geometric characteristics such as curvature, area, or surface normal variations. By adjusting this parameter locally rather than using a uniform density, the system achieves high fidelity in critical regions while minimizing the total number of points, thus reducing memory consumption and processing requirements.

The patent reduces processing resources by applying local quality to the sampling process. Instead of uniformly processing all points with high density, the system identifies and processes only those regions requiring high fidelity (such as high curvature areas or regions with significant geometric features). This selective approach maintains representation fidelity where needed while significantly reducing the total computational burden.

The patent applies partial action by performing dense sampling only in specific regions where it is geometrically necessary, rather than applying dense sampling uniformly across the entire curve or surface. This partial application of dense sampling maintains adequate representation fidelity for critical features while reducing the overall processing resources required.