3D Point Cloud Encoding Using N-ary Tree Prediction Modes
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Solution Overview
Problem
Current three-dimensional data encoding methods lack efficiency in compressing and transmitting large amounts of point cloud data, which is essential for applications like autonomous vehicles and infrastructure inspection, due to inadequate encoding and decoding techniques.
Innovation Solution
A three-dimensional data encoding method that utilizes an N-ary tree structure, where nodes are encoded using inter and intra prediction based on reference points, optimizing the encoding process by adjusting the prediction mode and reducing specification information, thereby improving coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is compressed using conventional encoding methods, then data transmission is enabled, but coding efficiency is insufficient and large amounts of data still require significant bandwidth
Solution Approach 1:
The point cloud data is segmented into multiple frames, with each frame containing multiple layers. The encoding process divides nodes into first nodes (encoded using inter prediction) and second nodes (encoded using intra prediction), allowing differential processing that improves overall coding efficiency while managing data volume systematically
Solution Approach 2:
The patent introduces a temporal dimension by utilizing reference frames from different time points (inter prediction) in addition to spatial relationships within the same frame (intra prediction). This multi-dimensional reference approach enables more effective compression by exploiting both temporal and spatial redundancies in the point cloud data
2Quantity of substance
If all nodes are encoded using inter prediction with reference frames, then compression ratio improves, but decoding complexity and specification information increase
Solution Approach 1:
Different prediction methods are applied to different nodes based on their local characteristics. First nodes are encoded using inter prediction while second nodes use intra prediction, allowing the system to adapt the encoding strategy to local data properties and reduce overall complexity by avoiding uniform complex processing across all nodes
Solution Approach 2:
Instead of applying inter prediction to all nodes, the patent selectively applies it only to first nodes where it provides benefit, while using simpler intra prediction for second nodes. This partial application of the more complex inter prediction method reduces decoding complexity while maintaining compression effectiveness
3Measurement precision
If more specification information is included to identify reference nodes, then encoding precision improves, but bitstream size and transmission overhead increase
Solution Approach 1:
The patent extracts only the essential specification information needed for reference node identification, separating it from the main bitstream. By taking out only the necessary identification data and using efficient encoding schemes, the system maintains precise reference node identification while minimizing the overhead added to the bitstream
Data Source
AI summary
A three-dimensional data encoding method includes: encoding nodes in an N-ary tree structure of a current three-dimensional point cloud, N being an integer greater than or equal to 2; and generating a bitstream including the nodes encoded. In the encoding: at least one node among the nodes is encoded using an inter prediction mode in which a reference three-dimensional point is referred to; and at least one other node among the nodes is encoded using an intra prediction mode in which the reference three-dimensional point is not referred to, the at least one other node being other than the at least one node. The bitstream further includes information which distinguishes between the at least one other node and the at least one node.