Point Cloud Coding Node Occupancy Prediction
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Solution Overview
Problem
Conventional point cloud coding techniques face inefficiencies in compressing and decompressing point cloud data, particularly in handling sparse distributions, where existing methods do not effectively utilize occupancy information of neighboring nodes to improve coding performance.
Innovation Solution
The proposed method predicts the occupancy indication of nodes and sub-nodes based on the occupancy states of neighboring nodes, preceding nodes, and density values, enhancing the efficiency of point cloud coding by using these predictions to inform the conversion process between point cloud frames and bitstreams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional point cloud coding techniques are used, then the coding process is simple, but the coding efficiency is low especially for sparse distributions
Solution Approach 1:
The patent applies preliminary action by predicting occupancy indications of nodes before actual coding. The system determines occupancy states of neighboring nodes and uses these predictions to inform the coding process, allowing efficient compression by anticipating which nodes need to be encoded and their likely occupancy states, thereby improving coding efficiency without requiring complex real-time analysis during encoding
Solution Approach 2:
The patent implements feedback mechanisms where occupancy information from neighboring nodes is used to refine the coding decisions. The system continuously references occupancy states of neighboring nodes to adjust prediction models, creating a feedback loop that improves coding efficiency by adapting to the specific spatial distribution patterns of point cloud data, particularly effective for sparse distributions
2Productivity
If occupancy information of neighboring nodes is utilized, then coding efficiency improves, but the complexity of determining occupancy states increases
Solution Approach 1:
The patent applies segmentation by dividing the point cloud data into hierarchical node structures (e.g., octree nodes). This segmentation allows the system to process occupancy information in manageable units, determining occupancy states at different levels of the hierarchy. By segmenting the data, the complexity of analyzing entire point clouds is reduced to analyzing smaller, manageable node structures while still capturing global occupancy patterns
Solution Approach 2:
The patent implements local quality by treating different nodes differently based on their occupancy states and spatial relationships. The system determines occupancy indications specifically for each node based on its neighboring nodes' occupancy states, allowing localized optimization of coding parameters. This approach improves compression efficiency for sparse regions without unnecessarily processing dense regions with high complexity
Data Source
AI summary
Embodiments of the present disclosure provide a solution for point cloud coding. A method for point cloud coding is proposed. The method comprises: determining, during a conversion between a current frame of a point cloud sequence and a bitstream of the point cloud sequence, an occupancy state of a first node of the current frame, a node representing a spatial partition of the current frame, the occupancy state of the first node representing whether the first node is occupied by a point; determining a prediction of an occupancy indication of a second node of the current frame, the occupancy indication indicating an occupancy state of the second node; and performing the conversion based on the prediction of the occupancy indication.