Intraoral 3D Scanning with High-Density Pattern Matching

The patent divides the dense light pattern into multiple smaller sub-patterns or regions, each processed independently to solve the correspondence problem. This segmentation reduces the computational complexity by breaking down the large-scale correspondence matching into smaller, more manageable tasks that can be processed in parallel or sequentially with reduced memory and processing requirements.

The system performs preliminary processing of the light pattern by pre-identifying and tagging specific feature points or markers within the dense pattern before the correspondence problem arises. This preliminary action creates a simplified reference framework that guides the subsequent correspondence matching, reducing the search space and computational burden when handling high-density patterns.

The patent applies different processing strategies to different regions of the light pattern based on local characteristics. High-density regions are processed with specialized algorithms optimized for their specific density and geometric properties, while less critical regions use standard processing. This local quality approach maintains high scan resolution where needed while reducing overall computational complexity.

The system performs preliminary organization and categorization of light pattern features before full image generation, pre-computing transformation matrices and feature descriptors for high-density regions. This preliminary action prepares the data in a format that simplifies subsequent processing steps, reducing the complexity of generating images from high-density patterns.

The patent segments the correspondence problem into multiple independent sub-problems that can be solved in parallel, enabling real-time processing of high-density patterns. By dividing the large-scale matching task into smaller regional matching tasks, the system achieves the computational throughput needed for real-time 3D representation while maintaining accuracy.

The system performs preliminary computation of transformation parameters and feature descriptors before the actual correspondence matching is needed. This pre-processing creates a ready-to-use framework that enables rapid real-time processing when the full 3D representation is generated, separating the heavy computational lift from the time-critical rendering phase.