3D Point Cloud Encoding Using Shared N-ary Octree Patterns
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Solution Overview
Problem
Current three-dimensional data encoding methods face inefficiencies in coding processes, particularly in representing and transmitting large volumes of three-dimensional point cloud data, which hinders effective use in applications like autonomous vehicles and infrastructure inspection.
Innovation Solution
The proposed solution involves encoding information from N-ary tree structures and octree structures using common encoding patterns, reducing processing load by leveraging shared encoding patterns between the two structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If separate encoding patterns are used for N-ary tree structures and octree structures, then encoding flexibility is maintained, but processing load increases and coding efficiency decreases
Solution Approach 1:
The patent applies universality by designing a unified encoding pattern that can handle both N-ary tree structures and octree structures. The encoding pattern includes a general node information encoding section that works for any tree structure type, and a structure-specific extension section that adapts to the particular structure being encoded. This allows the same encoding framework to serve multiple structure types without requiring separate complete encoding systems, thereby reducing processing load while maintaining coding efficiency.
Solution Approach 2:
The encoding pattern is segmented into modular components: a common node information section that applies to all tree structures, and structure-specific sections that handle N-ary or octree particularities. This segmentation allows the encoder to efficiently process different tree structures by activating only the relevant segments, reducing overall processing complexity while maintaining high coding efficiency for each structure type.
2Quantity of substance
If compression is applied to reduce data transmission size, then transmission efficiency improves, but decoding complexity increases
Solution Approach 1:
The encoding pattern incorporates feedback mechanisms where the encoded data includes structural metadata that guides the decoding process. The compressed data stream contains information about the tree structure type and node relationships that enable the decoder to reconstruct the original structure efficiently without requiring complex external processing. This feedback approach reduces decoding complexity while maintaining high compression ratios.
3Measurement precision
If detailed node information is encoded for each node, then data accuracy is maintained, but encoding time increases
Solution Approach 1:
The patent merges the encoding of common node information across the entire tree structure into a single efficient pass. Instead of processing each node independently with full detail, the encoder identifies and encodes shared structural properties at the tree level, then processes only the unique node-specific information. This merging approach maintains complete data accuracy while significantly reducing total encoding time.
Data Source
AI summary
A three-dimensional data encoding method includes: encoding first information of a first current node or second information of a second current node, the first current node being included in an N-ary tree structure of first three-dimensional points included in a first three-dimensional point cloud, N being 2 or 4, the second current node being included in an octree structure of second three-dimensional points included in a second three-dimensional point cloud The encoding of the first information or the second information includes encoding the first information using a first encoding pattern including a pattern common to a second encoding pattern used in encoding the second information.