Adaptive Point Cloud Attribute Coding for Efficient Compression

The patent applies local quality by transitioning from uniform fixed-rate sampling to adaptive sampling that considers local point distribution characteristics. The method calculates local density metrics for different regions of the point cloud and adjusts sampling rates accordingly, allowing higher sampling in dense regions and lower sampling in sparse regions, thereby improving compression efficiency while maintaining representation quality.

The patent implements dynamics by making the sampling rate adaptive rather than static. The sampling strategy dynamically adjusts based on local point density calculations, where the sampling rate varies across different spatial regions according to their specific characteristics, enabling the system to respond to local variations in point distribution.

The patent applies preliminary action by pre-calculating local density metrics and identifying optimal sample points before the actual compression process. The method performs initial analysis to determine regions of high and low density, pre-selects representative points, and prepares sampling strategies in advance, reducing the computational burden during real-time compression operations.

The patent implements self-service by enabling the point cloud data structure to guide its own sampling process. The algorithm uses intrinsic properties of the point cloud (such as spatial coordinates and density distributions) to automatically determine sampling rates and select sample points without requiring external intervention or complex manual configuration.