Point Cloud Attribute Prediction Using Adaptive Neighbor Lists
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Solution Overview
Problem
Existing methods for coding and decoding point cloud attribute information fail to consider scenario discontinuity and correlation between information types, leading to low prediction accuracy, large residuals, and frequent outliers.
Innovation Solution
Create an adaptive prediction list by selecting and updating points based on multiple types of information, including depth, spatial position, and azimuth, to optimize prediction modes and residuals, and design an entropy coding context model for improved efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If attribute information is predicted based on neighboring points in geometric space, then prediction can be performed, but prediction accuracy is low and residuals are large due to scenario discontinuity
Solution Approach 1:
The prediction list is dynamically updated by removing outliers and hop values based on statistical thresholds. The system adapts the composition of the prediction list during encoding, adjusting which neighboring points contribute to predictions based on their reliability, thereby improving accuracy while reducing residuals
Solution Approach 2:
The system uses feedback from residual analysis to identify and remove problematic points from the prediction list. By monitoring prediction errors and identifying outliers and hop values, the system continuously refines the prediction list to improve subsequent predictions and reduce information loss
2Measurement precision
If multiple prediction modes are used to handle scenario discontinuity, then prediction accuracy improves, but coding complexity increases
Solution Approach 1:
The prediction process is segmented into distinct modes: standard prediction using the prediction list, outlier handling with alternative predictions, and hop value detection with special processing. This segmentation allows the system to apply appropriate complexity only where needed rather than uniformly across all points
Solution Approach 2:
The patent introduces intermediary structures including a refined prediction list that filters out problematic points, and intermediary processing steps that detect and handle outliers and hop values. These intermediaries simplify the overall coding process by pre-processing the prediction data before final encoding
3Productivity
If geometric-based prediction is used without considering information correlation, then coding process is simple, but coding efficiency is reduced
Solution Approach 1:
The patent merges geometric spatial relationships with attribute information correlation by combining multiple types of neighboring points (spatial neighbors, depth neighbors, azimuth neighbors) into a unified prediction list. This integration improves coding efficiency by leveraging multiple information sources simultaneously
Solution Approach 2:
The prediction list is constructed as a composite structure combining points from different spatial relationships and attribute correlations. This composite approach integrates geometric proximity with attribute similarity, creating a more robust prediction foundation that improves coding efficiency
Data Source
AI summary
A method for coding includes: obtaining original point cloud data; creating an adaptive prediction list of the attribute information of the point cloud; selecting a prediction mode from the adaptive prediction list and predicting the attribute information of the point cloud, to obtain a predicted residual; and coding the prediction mode and the predicted residual, to obtain codestream information.