Point Cloud Grouping for Attribute Prediction Efficiency
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Solution Overview
Problem
The encoding and decoding efficiency of point cloud attribute information is not high due to the large quantity of attribute information in point cloud data, which poses a challenge in transmission efficiency.
Innovation Solution
A method and apparatus that utilize a point cloud group as an attribute prediction unit, determining an attribute coding mode for each point cloud group, predicting attribute information, and encoding or decoding based on prediction residual information to improve encoding and decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud attribute information is encoded and transmitted individually for each point, then transmission completeness is ensured, but encoding and decoding efficiency deteriorates due to the large quantity of attribute information
Solution Approach 1:
The patent divides the point cloud data into multiple point cloud groups, where each group contains multiple points. Attribute prediction is performed at the group level rather than individual point level, significantly reducing the number of prediction operations required. This segmentation approach maintains transmission completeness while improving encoding and decoding efficiency by processing groups of points simultaneously.
Solution Approach 2:
The patent combines multiple points into point cloud groups and performs unified attribute prediction on each group. By merging the processing of individual points into group-level operations, the patent reduces the overall quantity of encoding and decoding operations, thereby improving efficiency while preserving all necessary attribute information through the group structure.
2Loss of time
If attribute prediction is performed for each point individually, then prediction precision is maintained, but processing time increases due to the large number of points
Solution Approach 1:
The patent segments the point cloud into groups and performs attribute prediction on each group as a unit. This segmentation reduces the total number of prediction operations from individual point level to group level, significantly reducing processing time while maintaining prediction precision through the use of representative points within each group for prediction calculations.
Solution Approach 2:
The patent uses attribute information from representative points within each point cloud group to predict attributes for other points in the same group. This copying approach allows efficient propagation of attribute information across multiple points without performing individual predictions for each point, thereby reducing processing time while maintaining prediction accuracy.
3Quantity of substance
If point cloud data is compressed to reduce data volume, then transmission bandwidth is reduced, but encoding and decoding complexity increases
Solution Approach 1:
The patent segments point cloud data into groups and performs compression at the group level using attribute prediction. This segmentation approach simplifies the encoding process by reducing the number of individual encoding operations required, thereby reducing encoding and decoding complexity while achieving effective data volume reduction through group-based compression.
Solution Approach 2:
The patent changes the processing parameter from individual point attributes to group-level attribute predictions. By transforming the encoding approach to work with group representations rather than individual point data, the patent reduces data volume while maintaining manageable encoding and decoding complexity through the use of prediction residual information.
Data Source
AI summary
A point cloud decoding method is provided. In the method, a target point cloud group of a plurality of point cloud groups is obtained. The target point cloud group includes at least one point. An attribute coding mode of the target point cloud group is determined. Prediction attribute information of each point in the target point cloud group is obtained based on attribute prediction of the respective point in the target point cloud group according to the attribute coding mode of the target point cloud group. Reconstruction residual information of each point in the target point cloud group is obtained based on attribute decoding of the respective point in the target point cloud group. Reconstruction attribute information of each point in the target point cloud group is determined according to the prediction attribute information and the reconstruction residual information of the respective point in the target point cloud group.