Point Cloud Transmission Using Prediction Units and Motion Vectors
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing, transmitting, and compressing large amounts of point cloud data, particularly in addressing latency, encoding/decoding complexity, and improving compression performance for point cloud data captured by LiDAR equipment.
Innovation Solution
The method involves encoding geometry data and attribute data of point cloud data, partitioning the geometry data into prediction units using block size information, and applying motion vectors for inter-predictive compression, with devices including geometry and attribute encoders and transmitters for efficient transmission and reception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is processed and transmitted without partitioning, then the data can be handled as a whole, but the encoding complexity and processing time increase significantly
Solution Approach 1:
The point cloud data is partitioned into multiple prediction units (PUs) based on block size information. Each PU can be encoded and processed independently, which reduces the overall encoding complexity and enables parallel processing, thereby improving encoding speed without sacrificing compression efficiency
2Productivity
If motion vectors are applied to all prediction units, then inter-predictive compression is maximized, but the processing time and computational load increase
Solution Approach 1:
Motion vectors are selectively applied to prediction units based on their specific characteristics and requirements. Not all PUs require motion vector application, so the system applies motion compensation only where beneficial, optimizing compression efficiency while minimizing unnecessary computational overhead and processing time
3Productivity
If geometry data is partitioned into prediction units with different block sizes, then compression performance improves, but the signaling complexity increases
Solution Approach 1:
The system uses block size information as a parameter to define prediction unit boundaries. By changing the block size parameter adaptively, the system can create PUs of different sizes to optimize compression for various point cloud regions while managing signaling complexity through a systematic approach to parameter encoding
4Productivity
If point cloud data is transmitted without efficient partitioning, then the transmission can be simpler, but latency increases and real-time processing becomes difficult
Solution Approach 1:
Partitioning point cloud data into prediction units enables parallel processing and reduces transmission latency. Each PU can be encoded, transmitted, and processed independently, allowing for more efficient real-time processing while the block size information provides a structured approach to managing complexity
Data Source
AI summary
According to embodiments, disclosed are a point cloud data transmission method, a cloud data transmission device, a cloud data reception method, and a cloud data reception device. The point cloud data transmission method according to embodiments may comprise 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, wherein the step of encoding the geometry data comprises a step of dividing, according to block size information, the geometry data into one or more prediction units.