Point Cloud Attribute Coding Using Hilbert-Order Neighbors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing point cloud attribute coding methods based on Morton order suffer from poor spatial neighbor characteristics, leading to inaccurate attribute predictions due to periodic jumps in three-dimensional spatial distances, which affects coding efficiency.
Innovation Solution
Employing Hilbert order for point cloud attribute decoding to find nearest neighbors in three-dimensional space, using the weighted average of reconstructed attribute values of preceding decoding points to improve prediction accuracy and coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If Morton order is used for point cloud attribute coding, then the processing is convenient and accuracy is controllable, but the spatial neighbor characteristics are poor leading to inaccurate attribute predictions
Solution Approach 1:
The patent changes the ordering parameter from Morton order to Hilbert order. Hilbert order provides better spatial neighborhood characteristics while maintaining the benefits of systematic processing. This parameter change resolves the contradiction by improving prediction accuracy without sacrificing processing convenience.
Solution Approach 2:
The patent transitions from a one-dimensional Morton code ordering to a two-dimensional Hilbert curve ordering that better captures three-dimensional spatial relationships. This dimensional change allows the system to maintain processing structure while improving spatial neighbor characterization for accurate attribute prediction.
2Device complexity
If Morton order is used to find nearest neighbors, then the coding process is simplified, but the three-dimensional spatial distance jumps periodically causing poor neighbor selection
Solution Approach 1:
The patent changes the ordering parameter from Morton code to Hilbert code, which eliminates the periodic jumping behavior in three-dimensional spatial distance. This parameter change maintains coding simplicity while significantly improving neighbor selection reliability.
Solution Approach 2:
The patent converts the harmful periodic jumping behavior of Morton order into a beneficial continuous spatial progression of Hilbert order. The Hilbert curve's space-filling property ensures that adjacent points in the ordering are always spatially close, transforming a defect into a strength.
3Measurement precision
If the number of nearest neighbors is increased for better prediction, then the prediction accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent changes the ordering parameter to Hilbert order, which improves the quality of nearest neighbor candidates. This parameter change allows the system to achieve better prediction accuracy with fewer neighbors, thereby reducing computational complexity while improving precision.
Data Source
AI summary
In a point cloud attribute decoding method and device, Hilbert order is used as the coding order of point cloud attributes. The nearest neighbor points in the three-dimensional space of the current point are found in several points before the current point and are used as the prediction points of the current point. In the worst case, the distance in the three-dimensional space of the two points with a distance of n on the Hilbert order is less than or equal to n1/2, while the distance in the three-dimensional space of the two points with a distance of n on the Morton order may be greater than the maximum dimension length of the point cloud. The average distance between K points in the preceding order and the current point in Hilbert sequence is smaller than that between K points in the preceding order and the current point in Morton sequence.