Point Cloud Attribute Compression via Hierarchical LOD and Correction Encoding

The patent segments point cloud data into multiple levels of detail (LODs) based on spatial hierarchy. Points are organized in a hierarchical structure where coarse-level points represent overall structure and fine-level points add detailed information. This segmentation allows selective transmission of only necessary detail levels, reducing transmission time while maintaining data integrity for critical regions.

The patent applies local quality by differentiating compression levels across different spatial regions. Areas with high geometric complexity or important semantic information receive higher quality reconstruction, while uniform or less important regions use lower quality. This is achieved through adaptive attribute prediction and correction mechanisms that focus computational resources where needed most.

The patent applies preliminary action by pre-computing attribute prediction models during an encoding phase. Neighboring points are identified and their attribute relationships are established in advance, creating lookup tables and prediction parameters that simplify the actual compression process. During decoding, these pre-computed models enable efficient reconstruction without complex real-time calculations.

The patent introduces an intermediary correction value mechanism that mediates between predicted and actual attribute values. Instead of directly storing complex attribute data, the system stores compact correction values that adjust predicted values to match actual data. This intermediary representation significantly reduces data size while maintaining accuracy, balancing compression efficiency with processing complexity.

The patent implements nested doll by organizing point cloud data in a nested hierarchical structure where fine-level detail points are contained within coarse-level regions. Each level of the hierarchy is stored as a separate data structure that can be independently accessed. This nesting allows the system to store comprehensive detailed information while enabling selective retrieval of only the necessary detail levels, reducing storage requirements without sacrificing reconstruction accuracy when needed.

The patent applies dynamics by making the level of detail dynamically adjustable based on application requirements. The hierarchical structure enables the system to adapt the reconstruction accuracy dynamically - using coarse levels for low-precision applications to minimize storage, and switching to fine levels when high accuracy is required. This dynamic adaptability resolves the contradiction between storage efficiency and reconstruction accuracy.