Point Cloud Compression with Adaptive Geometry Quantization
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Solution Overview
Problem
Current point cloud compression technologies face challenges in efficiently encoding and decoding 3D geometry information due to fixed quantization parameters, which limit adaptive bit allocation and rate control, especially in applications requiring high precision and variable point densities.
Innovation Solution
The introduction of adaptive geometry quantization in point cloud coding allows for varying quantization parameters across different nodes of the octree partition structure, enabling dynamic bit allocation and improved coding efficiency through signaling of delta quantization parameters and use of look-up tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If fixed quantization parameters are used in point cloud compression, then device complexity is reduced and ease of operation is improved, but coding efficiency deteriorates and adaptability to varying point densities is limited
Solution Approach 1:
The patent implements dynamic quantization parameters that adapt to different spatial regions and octree nodes based on point density variations. The quantization parameter is no longer fixed but varies dynamically across the point cloud data structure, allowing the system to optimize compression efficiency for regions with different point densities while maintaining operational feasibility through automated adaptation.
Solution Approach 2:
The patent changes the quantization parameter values based on spatial position and octree node characteristics. By modifying the quantization parameter according to local point density and hierarchical structure, the system achieves better coding efficiency without requiring manual intervention, thus resolving the contradiction between ease of operation and coding efficiency.
2Device complexity
If fixed quantization parameters are used, then device complexity is reduced, but adaptability to varying point densities and rate control capability deteriorate
Solution Approach 1:
The patent segments the point cloud space into multiple octree nodes and spatial regions, allowing different quantization parameters to be applied to different segments. This segmentation enables the system to adapt to varying point densities in different regions while maintaining a structured approach that does not excessively increase device complexity.
Solution Approach 2:
The patent applies local quality by using different quantization parameters for different spatial regions and octree nodes based on their specific characteristics. Regions with high point density use different quantization settings compared to low-density regions, optimizing adaptability without requiring global complexity increases.
3Productivity
If adaptive geometry quantization with varying quantization parameters is implemented, then coding efficiency and adaptability are improved, but device complexity increases
Solution Approach 1:
The system implements dynamic quantization parameter selection based on octree node characteristics and spatial position. The decoder automatically adapts to the varying parameters through the hierarchical structure, managing complexity through the inherent organization of the octree rather than requiring additional complex management mechanisms.
Solution Approach 2:
The patent enables the decoding system to self-adapt to different quantization parameters through the octree structure and syntax elements embedded in the bitstream. The system automatically determines the appropriate quantization parameter for each node without external intervention, managing the increased complexity through automated self-service mechanisms.
4Adaptability or versatility
If adaptive geometry quantization is used to enable dynamic bit allocation, then rate control capability is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by establishing the octree structure and determining quantization parameters during the encoding phase. This preliminary organization of data and parameters allows the decoder to efficiently process the information without requiring complex real-time calculations, thus improving rate control capability while managing processing requirements through advance preparation.
Data Source
AI summary
Aspects of the disclosure provide methods, apparatuses, and a non-transitory computer-readable medium for point cloud compression and decompression. In a method, syntax information of a point cloud in a quantized space is decoded from a coded bitstream. The syntax information includes dividing information and adaptive geometry quantization information for a bounding box of the point cloud. The bounding box of the point cloud is divided into a plurality of parts based on the dividing information. Quantization parameters for the parts in the bounding box are determined based on the adaptive geometry quantization information. Points in each of the parts in the bounding box of the point cloud are reconstructed based on the quantization parameter for the respective part in the bounding box.