Point Cloud Geometry Coding with Adaptive Model Selection
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Solution Overview
Problem
Existing technologies face challenges in efficiently compressing and transmitting volumetric visual data, particularly point clouds, due to their large data size and the need for specialized compression schemes that account for the unique characteristics of point cloud data.
Innovation Solution
The proposed solution involves representing point clouds using a combination of triangle-based and tree-based models. Dense portions of the point cloud are represented using a triangle-based model like TriSoup, while non-dense portions are represented using a tree-based model like a space-partitioning tree. This approach allows for improved compression efficiency and modeling capability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single model is used to represent the entire point cloud, then the device complexity is reduced, but the compression efficiency and modeling capability deteriorate due to inability to adapt to varying density regions
Solution Approach 1:
The point cloud is divided into multiple sub-volumes based on spatial partitioning, and each sub-volume is independently assigned a suitable model (tree-based or triangle-based) according to its density characteristics. This segmentation allows different regions to be coded with optimal models, improving overall compression efficiency while managing complexity through localized processing.
Solution Approach 2:
Different regions of the point cloud are represented using different models tailored to their specific characteristics. Dense regions use triangle-based models for efficient representation, while sparse regions use tree-based models. This local adaptation optimizes compression performance for each region without requiring a completely different system for each case.
2Productivity
If tree-based models are used for all portions, then the device complexity is reduced, but the compression efficiency deteriorates for dense portions that can be better represented by triangle-based models
Solution Approach 1:
The system changes the modeling parameter (model type) based on the density parameter of each sub-volume. By evaluating point density and selecting appropriate models accordingly, the system optimizes compression efficiency for each region. This parameter adaptation allows the system to achieve high compression ratios without requiring manual intervention or overly complex decision-making processes.
Data Source
AI summary
Systems, apparatuses, methods, and computer-readable media are described for coding point cloud geometry using selected models. Dense portions of a point cloud may be coded using one model (e.g., a triangle-based model such as a TriSoup model), and non-dense portions may be coded using another model (e.g., a tree-based model such as a space-partitioning tree). Each model may be selected from a list of a plurality of models. Model information for each portion of the point cloud may be encoded or decoded. By representing portions of the point cloud using models, compression efficiency and/or modeling capability may be improved.