Point Cloud Compression via Patch Segmentation and Hybrid Encoding
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing large amounts of point cloud data due to latency and encoding/decoding complexity, which is crucial for providing high-quality services such as virtual reality (VR), augmented reality (AR), and self-driving applications.
Innovation Solution
A method and device for encoding and transmitting point cloud data through bitstreams, utilizing geometry-based and video-based point cloud compression techniques, along with predictive tree-based compression to reduce latency and improve encoding/decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If geometry-based point cloud compression is used, then encoding complexity is reduced, but compression efficiency deteriorates
Solution Approach 1:
The point cloud data is divided into multiple patches or regions, and each patch is processed independently through geometry-based compression. This segmentation allows the system to use simpler encoding methods for each small region while maintaining overall compression efficiency through selective application of more advanced techniques on specific patches that benefit from them.
Solution Approach 2:
The patent applies different compression strategies to different regions of the point cloud data based on local characteristics. Geometry-based compression is applied where it provides sufficient efficiency, while video-based or hybrid methods are used in regions requiring higher compression ratios, optimizing the balance between complexity and efficiency locally rather than uniformly across the entire dataset.
2Loss of information
If video-based point cloud compression is used, then compression efficiency is improved, but latency increases
Solution Approach 1:
The system performs preliminary geometry-based compression on point cloud data before applying more computationally intensive video-based compression techniques. This preliminary processing reduces the data volume and complexity upfront, allowing subsequent video-based compression to achieve higher compression ratios without excessive latency, as the input data has already been pre-processed and organized.
Solution Approach 2:
The patent implements a dynamic compression system that adapts the compression method and intensity based on real-time requirements such as latency constraints, available bandwidth, and importance of different point cloud regions. When latency is critical, the system favors geometry-based methods; when compression efficiency is prioritized and latency can be tolerated, video-based methods are applied, creating a flexible, situation-aware compression pipeline.
3Loss of information
If hybrid compression techniques are applied, then overall compression efficiency is improved, but encoding complexity increases
Solution Approach 1:
The hybrid compression system divides the point cloud into multiple patches and applies different compression techniques to different patches based on their characteristics and requirements. This segmentation allows the system to manage encoding complexity by processing smaller, independent regions with appropriate methods rather than applying complex hybrid techniques uniformly across the entire point cloud, reducing overall computational burden while maintaining efficiency where needed.
Data Source
AI summary
A point cloud data transmission method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting a bitstream comprising the point cloud data. A point cloud data reception method according to embodiments may comprise the steps of: receiving a bitstream including point cloud data; and decoding the point cloud data.