Point Cloud Attribute Encoding Multilayer Structure
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Solution Overview
Problem
Existing point cloud attribute encoding methods using prediction methods have limited space utilization, leading to inefficient encoding and decoding processes due to sequential encoding and reliance on previous point information.
Innovation Solution
A method involving sorting point cloud data, constructing a multilayer structure based on distances, and assigning direct, predictive, or transform encoding modes to each node, allowing for efficient encoding and decoding by utilizing information from neighboring nodes and transform matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If prediction method is used to encode point cloud attributes sequentially, then encoding process can be simplified, but space utilization range is limited and encoding efficiency is low
Solution Approach 1:
The patent divides the point cloud data into multiple groups based on spatial relationships and encodes them independently. Each group is processed as a unit, allowing parallel encoding operations and improving overall encoding efficiency while maintaining manageable complexity through structured segmentation.
Solution Approach 2:
The patent introduces a multilayer structure that adds a hierarchical dimension to the encoding process. By organizing points into layers based on spatial distance and relationships, the system transforms the traditional sequential 1D encoding approach into a multi-dimensional processing framework, enabling better space utilization and improved encoding efficiency.
2Ease of operation
If sequential encoding is used for point cloud attributes, then processing order is simple, but space utilization range is small
Solution Approach 1:
The patent segments the point cloud into multiple spatial groups and processes them in parallel rather than sequentially. This segmentation allows the system to utilize more space for encoding operations while maintaining simple processing logic through structured group management and independent encoding of each group.
Solution Approach 2:
The patent implements a dynamic encoding approach where the processing order and structure can adapt based on the spatial relationships and distance thresholds. The multilayer structure allows flexible organization of points based on their spatial positions, enabling the system to optimize space utilization while maintaining operational simplicity through adaptive processing strategies.
3Ease of manufacture
If prediction based on previous point information is used, then encoding can be completed based on predicted value, but encoding efficiency is not improved
Solution Approach 1:
The patent segments the encoding process into independent group operations, where each group is encoded using prediction based on its own internal point relationships rather than relying solely on previous points. This segmentation enables parallel processing and improves encoding efficiency while maintaining the prediction-based approach for completing encoding of each group.
Solution Approach 2:
The patent adds a multilayer hierarchical structure that introduces a new dimension to the prediction process. By predicting attribute values based on spatial relationships across multiple layers rather than just sequential order, the system improves encoding efficiency while maintaining the ease of completing encoding through predicted values.
Data Source
AI summary
A point cloud attribute encoding method and apparatus, decoding method and apparatus are disclosed. The point cloud attribute encoding method includes: sorting point cloud data to be encoded to obtain sorted point cloud data; constructing a multilayer structure based on the sorted point cloud data and distances between the sorted point cloud data; obtaining an encoding mode corresponding to each of nodes in the multilayer structure. The encoding mode corresponding to each of the nodes is a direct encoding mode, a predictive encoding mode, or a transform encoding mode. The predictive encoding mode is to encode a node based on information of a neighboring node corresponding to the node. The transform encoding mode is to encode the node based on a transform matrix; and encoding point cloud attributes for each of the nodes based on the multilayer structure and the respective encoding mode.