Point Cloud Metadata Encoding for Low-Bitrate Reconstruction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies face challenges in efficiently compressing dynamic point clouds for distribution while maintaining high quality and reducing bit-rate consumption, particularly in applications like virtual reality and autonomous vehicles, where point clouds are often large and require real-time processing.
Innovation Solution
A two-layer-based encoding structure is employed, comprising a base layer for lossy representation and an enhancement layer for lossless details, using existing video codecs to compress geometry and texture information, along with metadata encoding for patch information and occupancy maps to optimize compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If dynamic point clouds are compressed using conventional methods, then bit-rate consumption is reduced, but reconstruction quality deteriorates
Solution Approach 1:
The patent divides the point cloud compression into two independent layers: a base layer for lossy compression and an enhancement layer for lossless details. This segmentation allows the base layer to achieve high compression ratios while the enhancement layer preserves critical quality information, resolving the contradiction between bit-rate reduction and reconstruction quality.
Solution Approach 2:
The enhancement layer selectively encodes only the most important geometric and attribute information that contributes to reconstruction quality. By applying local quality principles, the system maintains high quality where needed while using minimal bits, thus reducing overall bit-rate consumption without sacrificing reconstruction accuracy.
2Manufacturing precision
If point cloud data is transmitted at high quality, then reconstruction fidelity is improved, but distribution efficiency deteriorates
Solution Approach 1:
The two-layer structure enables a decoder to access only the base layer for efficient distribution scenarios, while optionally adding the enhancement layer for high fidelity requirements. This segmentation makes the system adaptable to different distribution efficiency and quality requirements simultaneously.
Solution Approach 2:
The enhancement layer contains more detail information than strictly necessary for basic reconstruction. By providing this excessive information selectively, the system allows receivers to achieve high fidelity when needed while maintaining efficient distribution by keeping the base layer alone for general purposes.
3Manufacturing precision
If lossless compression is applied to all point cloud data, then reconstruction quality is maintained, but bit-rate increases
Solution Approach 1:
The patent applies lossless compression only to the enhancement layer containing critical detail information, while the base layer uses lossy compression. This segmentation ensures that only the necessary bits are encoded losslessly, maintaining reconstruction quality while minimizing overall bit-rate consumption.
Solution Approach 2:
Lossless encoding is applied locally only to the enhancement layer where quality preservation is critical, rather than to the entire point cloud data. This local application of lossless compression maintains reconstruction quality where needed while significantly reducing the total bit-rate compared to full lossless encoding.
Data Source
Figure 1~2
Figure 3
Figure 3a
AI summary
At least one embodiment relates to a method for transmitting PLRM metadata that removes the dependency between PLRM metadata and the occupancy map and the bloc to patch information BlockToPatch.