Point Cloud Data Transmission Using Octree Encoding
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Solution Overview
Problem
The existing methods for processing point cloud data are inefficient due to high latency and complexity in encoding and decoding, particularly when dealing with large volumes of data required for applications like virtual reality, augmented reality, and self-driving services.
Innovation Solution
A method and apparatus for efficiently transmitting and receiving point cloud data by encoding geometry and attribute information using an octree structure, approximating geometry information, and predicting attribute information based on structural similarity, reducing computational complexity and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If traditional encoding methods are used for point cloud data, then compression is achieved, but encoding and decoding complexity increases significantly
Solution Approach 1:
The point cloud data is divided into multiple octants using an octree structure, where each node represents a specific spatial region. This segmentation allows independent processing of different data regions, reducing the overall computational complexity while maintaining compression effectiveness.
Solution Approach 2:
The patent transforms the three-dimensional point cloud data into a hierarchical octree structure, adding a structural dimension to the data representation. This dimensional transformation enables more efficient compression by exploiting spatial relationships and hierarchies that are not apparent in the original point cloud format.
2Loss of substance
If traditional encoding methods are used for point cloud data, then compression is achieved, but transmission latency increases
Solution Approach 1:
The patent performs preliminary organization of point cloud data into an octree structure at the encoding stage, pre-processing the data into a format that facilitates faster transmission and decoding. This preliminary structuring reduces the computational burden during real-time transmission and decoding operations.
Solution Approach 2:
By segmenting the point cloud data into hierarchical octree nodes, the patent enables progressive transmission where important regions can be sent first, reducing perceived latency for applications where immediate visualization is critical.
3Manufacturing precision
If detailed point cloud data is transmitted, then high quality is achieved, but bandwidth requirements increase
Solution Approach 1:
The patent extracts and transmits only the essential structural information through the octree occupancy codes and selective attribute data, rather than transmitting all raw point cloud coordinates. This extraction approach maintains visual quality while significantly reducing the data volume that needs to be transmitted.
Solution Approach 2:
The patent applies different levels of detail and compression to different regions of the point cloud based on their importance, allowing high quality preservation in critical areas while using more aggressive compression in less important regions, thereby optimizing the quality-to-bandwidth ratio.
Data Source
AI summary
A point cloud data transmission method according to embodiments comprises the steps of: acquiring point cloud data; encoding geometry information included in the point cloud data; encoding attribute information included in the point cloud data; and transmitting the encoded geometry information and the encoded attribute information.