Point Cloud Context Modeling With Local Occupancy and Plane Data
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Solution Overview
Problem
The existing Geometry-based Point Cloud Compression (G-PCC) frameworks require significant storage space due to the large amount of information needed for context modeling in the coding of geometric information, which hinders efficient encoding and decoding processes.
Innovation Solution
A method for encoding and decoding point clouds that reduces the amount of stored information by determining context models based on reduced parameter information, utilizing only plane and occupancy information of adjacent nodes, thereby optimizing storage space without compromising performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If context models are constructed using traditional methods in G-PCC framework, then encoding and decoding can be performed, but storage space consumption becomes excessively high
Solution Approach 1:
The patent extracts only the necessary information for context modeling from the full geometric data. Specifically, it uses only the occupancy information of adjacent nodes and plane information to construct context models, rather than storing all geometric attributes. This extraction approach maintains the essential functionality for encoding/decoding while dramatically reducing storage requirements.
Solution Approach 2:
The patent applies local quality by constructing context models based on local geometric properties (occupancy and plane information of adjacent nodes) rather than using global or comprehensive geometric data. Each context model is built from the specific local characteristics needed for that region's encoding, optimizing the balance between functionality and storage efficiency.
2Quantity of substance
If reduced parameter information is used for context modeling, then storage space is reduced, but encoding and decoding performance may be compromised
Solution Approach 1:
The patent changes the parameters used for context modeling from comprehensive geometric data to a reduced set of parameters (occupancy information and plane information of adjacent nodes). This parameter transformation maintains the essential information needed for accurate encoding/decoding while significantly reducing the data volume stored.
Solution Approach 2:
The patent creates simplified copies of the necessary geometric information for context modeling. Instead of storing the complete geometric data, it creates condensed representations (copies) that contain only the essential occupancy and plane information needed for context model construction, achieving both space reduction and performance maintenance.
Data Source
AI summary
A method for decoding includes: first parameter information of a current node is determined; context indication information of the current node is determined according to the first parameter information; a context model is determined according to the context indication information; and a bitstream is decoded based on the context model to determine plane position information of the current node in a preset direction.