Point Cloud Planar Prediction Using Neighborhood Occupancy
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Solution Overview
Problem
The existing methods for point cloud encoding result in poor predictive coding performance for planar structure information due to reliance on prior reference information, leading to inefficiencies in compressing large volumes of point cloud data.
Innovation Solution
The proposed method determines the validity of first-type neighborhood nodes and obtains occupancy information to perform predictive decoding/encoding on planar structure information, enhancing coding efficiency for planar nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If predictive coding is performed on planar structure information using only prior reference information, then the encoding process is simple, but the predictive coding performance is poor
Solution Approach 1:
The patent performs preliminary classification of neighborhood nodes into first-type (valid geometry information) and second-type (invalid or pending geometry information) nodes before predictive coding. This preliminary action enables the selection of appropriate reference information sources, improving prediction accuracy without significantly complicating the encoding process.
Solution Approach 2:
The patent applies different prediction strategies based on the local characteristics of neighborhood nodes. First-type nodes use their geometry information for prediction, while second-type nodes use alternative reference information. This localized quality adjustment optimizes predictive coding performance for planar structures while maintaining encoding simplicity.
2Quantity of substance
If a large number of points are transmitted in point cloud data, then the data completeness is high, but the transmission efficiency is low
Solution Approach 1:
The patent extracts and transmits only the essential planar structure information and occupancy data of neighborhood nodes, rather than transmitting all raw point cloud data. This extraction approach maintains data completeness for reconstruction while significantly reducing transmission volume and improving efficiency.
Solution Approach 2:
The patent segments the point cloud data into occupancy information and planar structure information, processing and transmitting them separately through different encoding paths. This segmentation enables efficient compression while preserving the completeness needed for accurate reconstruction.
3Productivity
If planar coding method is used for relatively planar nodes, then the coding efficiency of geometry information is improved, but the complexity of determining planar structures increases
Solution Approach 1:
The patent changes the parameter used for planar structure determination from complex geometric analysis to a simpler occupancy-based classification of neighborhood nodes. By using occupancy information to identify first-type and second-type nodes, the system achieves planar coding efficiency without the computational complexity of traditional planar structure detection.
Data Source
AI summary
The present disclosure provides a point cloud decoding method. The point cloud decoding method includes: determining first information corresponding to a current node, wherein the first information is used to indicate whether first-type neighborhood nodes of the current node are valid, and the first-type neighborhood nodes are neighborhood nodes whose geometry information has been decoded; obtaining occupancy information of N neighborhood nodes of the current node based on the first information, N being a positive integer; and performing predictive decoding on planar structure information of the current node based on the occupancy information of the N neighborhood nodes.