Point Cloud Data Transmission Using Octree Segmentation
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Solution Overview
Problem
Current methods for processing point cloud data face challenges in efficiently transmitting and receiving large volumes of data, leading to high computational complexity and latency, particularly in encoding and decoding processes.
Innovation Solution
A method and apparatus that encode geometry and attribute data of point cloud data using entropy coding units determined by a tree structure, Morton code, or level of detail, applying zero-run-length coding and arithmetic coding to improve compression efficiency and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional encoding methods are used for point cloud data, then the data can be transmitted, but the computational operation becomes complicated and time-consuming due to the large volume of data
Solution Approach 1:
The patent divides the point cloud data into multiple octree nodes based on spatial partitioning, and further segments the attribute data into multiple channels (e.g., color, reflectance, transparency). Each segment can be encoded and transmitted independently, reducing the computational complexity of processing the entire large dataset at once while maintaining high throughput through parallel processing of segments.
Solution Approach 2:
The patent applies different encoding strategies to different octree nodes and attribute channels based on their specific characteristics. Important regions with higher detail requirements are encoded with higher precision, while less important regions use lower precision encoding. This local quality approach optimizes the balance between compression efficiency and computational complexity.
2Productivity
If encoding and decoding operations are performed on large point cloud data, then data transmission is enabled, but latency increases
Solution Approach 1:
By segmenting the point cloud data into octree nodes and attribute channels, the patent enables parallel encoding and decoding operations that can proceed simultaneously in different regions. This segmentation allows for overlapping computation and transmission, significantly reducing latency while maintaining full data transmission capability.
Solution Approach 2:
The patent performs preliminary spatial partitioning of the point cloud data into octree nodes before encoding, allowing the receiving end to prepare decoding resources in advance based on the partition structure. This preliminary organization enables more efficient real-time decoding and reduces transmission latency.
3Device complexity
If attribute data is encoded without channel separation, then the encoding process is simpler, but compression efficiency is reduced
Solution Approach 1:
The patent separates attribute data into multiple channels (color, reflectance, transparency) and applies different encoding strategies to each channel based on its specific statistical characteristics and importance. This allows for optimized compression efficiency in each channel while managing overall encoding complexity through systematic organization.
Solution Approach 2:
The patent changes the encoding parameters and methods for different attribute channels based on their specific requirements. For example, color channels may use different quantization steps and entropy coding parameters compared to reflectance or transparency channels. This parameter adaptation significantly improves overall compression efficiency.
Data Source
AI summary
A point cloud data transmission method according to embodiments comprises the steps of: encoding geometry data of point cloud data; encoding attribute data of the point cloud data on the basis of the geometry data; and transmitting the encoded geometry data, the encoded attribute data and signaling data.