3D Point Cloud Encoding Adaptive Hierarchy
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Solution Overview
Problem
Current methods for encoding and decoding three-dimensional data, particularly point cloud data, face challenges in efficiently compressing and transmitting large amounts of data, leading to inefficiencies in data representation and processing.
Innovation Solution
A method that calculates an encoding coefficient by generating a hierarchical structure for three-dimensional points, sorting attribute information into higher and lower frequency components, and generating a bitstream accordingly, allowing for efficient encoding and decoding of three-dimensional data without a hierarchy structure when the number of points is small.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a hierarchical structure is generated for encoding three-dimensional points, then data compression efficiency is improved, but device complexity increases
Solution Approach 1:
The patent applies dynamics by making the encoding approach adaptive based on the number of three-dimensional points. When the number of points exceeds a threshold, hierarchical encoding is employed; otherwise, simple encoding is used. This dynamic selection optimizes compression efficiency while avoiding unnecessary complexity for small datasets.
Solution Approach 2:
The patent changes the encoding parameter (hierarchical structure generation) based on the data size parameter. By setting a threshold for the number of three-dimensional points, the system transitions between different encoding modes, achieving efficient compression only when beneficial while maintaining simplicity otherwise.
2Loss of substance
If hierarchical structure generation is applied to all point cloud data, then compression ratio is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by selectively applying hierarchical encoding only when the number of three-dimensional points exceeds a predetermined threshold. For smaller datasets, simple encoding is sufficient, avoiding the excessive processing time that hierarchical structure generation would incur without providing proportional benefits.
3Device complexity
If simple encoding is used for small point clouds, then device complexity is reduced, but compression efficiency deteriorates
Solution Approach 1:
The patent implements a dynamic encoding strategy that adapts to the scale of the point cloud data. By monitoring the number of three-dimensional points and comparing it against a threshold, the system dynamically selects the appropriate encoding method, ensuring compression efficiency is optimized for large datasets while maintaining simplicity for small ones.
Data Source
AI summary
A three-dimensional data encoding method includes: (i) when a number of three-dimensional points included in point cloud data to be encoded is n that is greater than a predetermined number, n being an integer greater than or equal to 2, calculating an encoding coefficient by generating a hierarchical structure in which each of n pieces of attribute information on the three-dimensional points is sorted into one of a higher frequency component and a lower frequency component to be layered, and generating a bitstream including the encoding coefficient calculated in the calculating; and (ii) when a number of three-dimensional points included in the point cloud data is m that is smaller than or equal to the predetermined number, m being an integer greater than or equal to 1, generating a bitstream in accordance with m pieces of attribute information on the three-dimensional points without generating a hierarchy structure.