Point Cloud Attribute Encoding via Spatial Correlation Grouping
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Solution Overview
Problem
Existing point cloud attribute compression methods group data based solely on one-dimensional order, which fails to effectively reflect correlations between point cloud data, leading to inefficient decorrelation and encoding efficiency.
Innovation Solution
A method that sorts point cloud data based on correlation, such as spatial position relationships, and groups them accordingly to enhance correlation within groups, followed by point cloud attribute encoding using techniques like Morton or Hilbert codes and discrete cosine transforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data are grouped according to one-dimensional order, then the grouping process is simple and fast, but the correlation between point cloud data within groups is weak, affecting decorrelation efficiency and encoding performance
Solution Approach 1:
The patent transitions from one-dimensional ordering to multi-dimensional spatial relationship-based grouping. By introducing spatial position relationships as a new dimension for grouping criteria, the method captures three-dimensional correlation structures that one-dimensional ordering cannot represent, thereby improving both correlation reflection accuracy and subsequent encoding efficiency
Solution Approach 2:
The patent performs preliminary sorting of point cloud data based on spatial relationships before grouping. This preliminary organization establishes a structured framework that enhances the effectiveness of subsequent grouping operations and decorrelation transforms, leading to improved encoding efficiency without sacrificing correlation capture
2Reliability
If point cloud data are grouped based on spatial correlation, then decorrelation efficiency is improved, but the grouping process becomes more complex
Solution Approach 1:
The patent segments the point cloud data into multiple groups based on spatial correlation characteristics. By dividing the data into manageable segments that share similar spatial relationships, the method simplifies the overall grouping process while maintaining effective correlation capture within each segment
Solution Approach 2:
The patent utilizes changes in spatial parameters (position relationships) as the basis for grouping. By transforming the grouping criterion from simple one-dimensional indices to multi-dimensional spatial parameters, the method achieves more effective grouping without requiring overly complex computation, balancing accuracy and complexity
Data Source
AI summary
A point cloud attribute encoding method and apparatus, a decoding method and apparatus, and a related device are disclosed. The point cloud attribute encoding method includes: sorting all point cloud data to be encoded to acquire sorted point cloud data, the point cloud data to be encoded being point cloud data with attributes to be encoded; grouping the sorted point cloud data based on correlation between the sorted point cloud data to acquire groups to be encoded; and performing point cloud attribute encoding based on the groups to be encoded. It is beneficial for enhancing the correlation between point cloud data within the group, improving the efficiency of decorrelation during intra group transform after grouping, and improving coding efficiency.