3D Point Cloud Prediction Tree Encoding for Duplicate Points
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Solution Overview
Problem
Existing three-dimensional data encoding and decoding methods lack efficiency in compressing and transmitting large amounts of point cloud data, leading to inefficiencies in network bandwidth usage and data storage.
Innovation Solution
The method involves adding a first information item to a bitstream indicating whether a duplicated point is available, and when available, adding second information items to indicate the total number of points in a prediction tree, reducing redundant data encoding by using a single node to represent multiple points with the same position information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is compressed using traditional encoding methods, then data transmission and storage become feasible, but coding efficiency remains insufficient leading to excessive data amounts
Solution Approach 1:
The patent merges multiple three-dimensional points that share the same position information into a single node in the prediction tree. Instead of encoding each point separately, the encoder combines points with identical coordinates and represents them through one shared node, thereby reducing the total number of nodes that require encoding and improving coding efficiency
Solution Approach 2:
The patent introduces a duplicated point mechanism where a single node serves multiple functions by representing both itself and other points with the same position information. This multi-functional node structure allows the system to handle variable numbers of points at each position efficiently, adapting to different data distributions without requiring separate encoding structures
2Loss of information
If all position information items are encoded separately for each three-dimensional point, then complete spatial representation is achieved, but redundant information encoding increases data amount
Solution Approach 1:
The patent combines position information from multiple three-dimensional points into a shared node when the points have identical coordinates. The prediction tree structure allows different nodes to share common ancestors, meaning position information is encoded once at the shared node and reused by all descendant nodes, eliminating redundant encoding of the same spatial coordinates
Solution Approach 2:
The patent segments the encoding process into two parts: shared position information encoded at common nodes in the prediction tree, and unique point-specific information encoded at individual leaf nodes. This segmentation allows the system to separate redundant spatial data from necessary point differentiation data, encoding only what is unique at each level
Data Source
AI summary
A three-dimensional data encoding method includes: adding a first information item to a bitstream, the first information item indicating whether a duplicated point is available, the duplicated point representing, using a single node, position information items of two or more three-dimensional points having a same position information item in a prediction tree showing a reference relation in predictive encoding; and adding second information items to the bitstream when the first information item indicates that the duplicated point is available, the second information items each corresponding to a different one of nodes included in the prediction tree, and indicating a total number of one or more three-dimensional points included in the different one of the nodes.