3D Surface Model Generation from Structured Point Clouds

The patent replaces traditional GPU-based parallel processing with a CPU-based algorithmic approach. The invention uses carefully designed data structures and algorithms that exploit CPU cache hierarchy and memory access patterns to achieve efficient 3D surface reconstruction without requiring specialized graphics hardware. This substitution of processing architecture resolves the contradiction by maintaining high productivity through optimized CPU utilization while reducing device complexity and cost.

The patent changes the processing parameters by transforming the point cloud data into a structured format with specific geometric properties. By organizing points into connected components and using hierarchical data structures, the algorithm optimizes memory access patterns and computational efficiency. This parameter transformation allows standard CPUs to achieve performance comparable to or exceeding GPU solutions for this specific task, resolving the contradiction between productivity and device complexity.

The patent divides the point cloud data into connected components and processes them hierarchically. By segmenting the surface into manageable regions and using progressive refinement algorithms, the system achieves high manufacturing precision for each segment while keeping the memory footprint of individual processing units small. This segmentation strategy allows accurate surface reconstruction without requiring the entire high-resolution model to be loaded into memory simultaneously.

The patent employs nested data structures where points are organized into hierarchical groups, with each level containing references to sub-levels. This nested organization allows the algorithm to process and store surface information at multiple resolutions, maintaining high manufacturing precision for detailed regions while using coarser representations for distant or less critical areas, thereby optimizing memory usage.

The patent performs preliminary actions by pre-processing the point cloud data to identify and remove duplicate points, filter noise, and establish initial geometric relationships before the main reconstruction algorithm. This preliminary organization of data structures and pre-computation of geometric properties significantly reduces the processing time required for the subsequent high-precision surface reconstruction, resolving the contradiction between measurement precision and processing time.

The patent implements a progressive refinement approach where the surface model is constructed in multiple passes, with each pass adding increasing levels of detail. The algorithm performs partial processing at coarser resolutions first, then progressively refines specific regions that require higher measurement precision. This selective refinement strategy achieves accurate surface reconstruction without the excessive processing time that would result from uniformly processing all points at maximum resolution.