3D Point Cloud Encoding via N-ary Tree Reference Limitation
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Solution Overview
Problem
Existing methods for encoding three-dimensional data are inefficient in reducing the amount of data in a bitstream, particularly for point cloud data which requires significant compression for effective transmission and storage.
Innovation Solution
A three-dimensional data encoding method that utilizes an N-ary tree structure to encode target nodes, where the encoding is based on reference limitation information indicating a referable neighbor node, and includes encoding processing information when the target node is encoded by reference to a neighbor node.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If three-dimensional point cloud data is encoded using conventional methods, then the data can be transmitted and stored, but the amount of data in the bitstream remains large requiring significant compression
Solution Approach 1:
The three-dimensional space is segmented into an N-ary tree structure where the encoding space is divided into multiple subspaces (nodes), and each node is independently encoded. This segmentation allows the encoder to process only relevant portions of the data and apply different encoding strategies to different regions, thereby reducing the overall data amount while maintaining encoding efficiency.
Solution Approach 2:
The patent applies different encoding methods to different nodes based on their specific characteristics. For each target node, the encoder selectively references only those neighbor nodes that are spatially adjacent and relevant, rather than using a uniform encoding approach across all nodes. This local adaptation reduces redundant data representation and improves compression ratios.
2Measurement precision
If reference limitation information and encoding processing information are always included in the bitstream, then decoding accuracy is improved, but the data amount increases
Solution Approach 1:
The patent implements partial action by conditionally including reference limitation information and encoding processing information in the bitstream based on whether the target node is actually encoded by reference to neighbor nodes. When reference encoding is not used, these additional information elements are omitted, reducing the data amount while maintaining decoding accuracy when needed.
Solution Approach 2:
The encoding approach is made dynamic by adapting the inclusion of reference information to the specific characteristics of each target node. The encoder determines on a per-node basis whether reference encoding is beneficial, and accordingly includes or excludes the relevant information in the bitstream. This dynamic adaptation optimizes the balance between decoding accuracy and data compression.
Data Source
AI summary
A three-dimensional data encoding method includes: encoding information of a target node included in an N-ary tree structure of three-dimensional points included in three-dimensional data; and generating a bitstream including the information of the target node encoded. In the encoding, the target node is encoded based on reference limitation information indicating a referable neighbor node among neighbor nodes spatially neighboring the target node. In the generating, when the target node is encoded by reference to information of a first neighbor node, the bitstream further including encoding processing information is generated, the encoding processing information indicating a processing method in the encoding; and when the target node is encoded without reference to the information of the first neighbor node, the bitstream is generated without including the encoding processing information in the bitstream.