Point Cloud Data Transmission Using Geometry-Based Compression
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Solution Overview
Problem
Current methods for processing point cloud data face challenges in efficiently handling large amounts of data, leading to latency and encoding/decoding complexity, particularly in applications like virtual reality, augmented reality, and self-driving services.
Innovation Solution
A method and device for encoding and decoding point cloud data using geometry-based and video-based compression coding, which involves transforming point cloud data into a bitstream for efficient transmission and reception, utilizing techniques such as octree geometry coding and attribute encoding to reduce data size while maintaining quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If point cloud data is transmitted using traditional compression methods, then data transmission is achieved, but latency and encoding/decoding complexity increase
Solution Approach 1:
The point cloud data is divided into multiple tiles or partitions, allowing parallel processing during encoding and decoding. This segmentation reduces the computational complexity for each processing unit while maintaining overall data fidelity, thereby reducing encoding/decoding time and latency
Solution Approach 2:
Geometry-based prediction is performed before attribute encoding, pre-establishing spatial relationships and prediction models. This preliminary geometry coding creates a framework that simplifies subsequent attribute compression, reducing overall encoding complexity and processing time
2Productivity
If point cloud data is compressed to reduce data size, then transmission efficiency improves, but encoding/decoding complexity increases
Solution Approach 1:
Geometry information serves as an intermediary between raw point cloud data and attribute compression. The geometry-based prediction model acts as a mediator that transforms spatial relationships into prediction residuals, enabling more efficient attribute encoding while maintaining a relatively simple decoding process
Solution Approach 2:
The method transforms the representation parameters of point cloud data by using geometry-based predictions to generate residual attributes. This parameter transformation changes the data from raw coordinates to prediction residuals, which have better compression characteristics and enable more efficient transmission
3Productivity
If geometry-based compression coding is used, then processing efficiency improves, but implementation complexity increases
Solution Approach 1:
The implementation divides the point cloud into manageable tiles and processes them independently, allowing parallel computation. This segmentation enables the complex geometry-based algorithms to be implemented efficiently using modern parallel processing architectures, reducing overall implementation complexity
Solution Approach 2:
Geometry-based prediction models are pre-computed and stored, allowing the system to benefit from complex geometric analysis without repeating the full computation during real-time processing. This preliminary geometry coding phase separates the complex computational work from the time-critical decoding phase
Data Source
AI summary
Disclosed herein are a point cloud data transmission method including encoding point cloud data, and transmitting a bitstream containing the point cloud data, and a point cloud data reception method including receiving a bitstream containing point cloud data, and decoding the point cloud data.