3D Data Encoding Using Spatial Range Constraints
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Solution Overview
Problem
Current methods for encoding three-dimensional data are inefficient in reducing processing requirements, particularly in representing and transmitting large datasets like point clouds, which are essential for applications such as autonomous vehicles and infrastructure inspection.
Innovation Solution
A three-dimensional data encoding method that utilizes an N-ary tree structure, where information about a current node is encoded by referencing neighboring nodes within a specific spatial range, allowing for reduced processing by limiting referable neighboring nodes and employing entropy encoding with selected coding tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all neighboring nodes are referenced for encoding current node, then encoding accuracy is improved, but processing amount increases
Solution Approach 1:
The patent applies local quality by differentiating the treatment of neighboring nodes based on their spatial relationship with the current node. Instead of uniformly processing all neighboring nodes, the invention selectively references only those nodes that fall within a predetermined range (e.g., adjacent octants in an octree structure). This selective approach maintains encoding accuracy for critical neighboring nodes while reducing processing overhead by excluding distant nodes, thereby resolving the contradiction between encoding precision and processing efficiency.
2Quantity of substance
If large amount of three-dimensional data is encoded, then data completeness is improved, but transmission and processing time increases
Solution Approach 1:
The patent employs segmentation by dividing the three-dimensional space into hierarchical structures such as octrees, where the space is recursively partitioned into smaller volumetric regions. This segmentation allows the encoding process to handle large datasets by processing them in manageable hierarchical segments rather than as a monolithic block. The hierarchical structure enables progressive encoding and transmission, where coarse-level segments can be processed and transmitted first, with finer details added subsequently, thereby reducing overall processing and transmission time while maintaining data completeness.
Solution Approach 2:
The invention applies preliminary action through predictive coding techniques that utilize information from already-encoded neighboring nodes to predict the values of current nodes. By performing this prediction beforehand and only encoding the residuals (differences between predicted and actual values), the system significantly reduces the amount of data that needs to be transmitted and processed. This preliminary predictive step maintains data completeness while substantially reducing transmission and processing time.
Data Source
AI summary
A three-dimensional data encoding method includes encoding information of a current node included in an N-ary tree structure of three-dimensional points included in three-dimensional data, where N is an integer greater than or equal to 2. In the encoding, first information is encoded, the first information indicating a range for one or more referable neighboring nodes among neighboring nodes spatially neighboring the current node, and the current node is encoded with reference to a neighboring node within the range.