Point Cloud Transmission With Octree Geometry and LoD Attribute Layers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for transmitting and receiving point cloud data are inefficient due to high computational complexity and latency, requiring improved encoding and decoding techniques to handle large volumes of data effectively.
Innovation Solution
The method involves encoding geometry data using an octree structure and compressing attribute data based on levels of detail (LoDs), with signaling information to manage sampling positions, allowing for efficient transmission and reception of point cloud data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is transmitted with high throughput to represent three-dimensional content, then the quality and detail of the point cloud content improve, but the computational complexity and encoding/decoding time increase significantly
Solution Approach 1:
The point cloud data is divided into multiple layers or groups of layers, where each layer represents a different level of detail or spatial resolution. This segmentation allows the receiver to selectively decode only the necessary layers based on available resources, reducing overall computational complexity while maintaining the ability to represent high-quality point cloud content when full decoding is performed.
2Measurement precision
If tens of thousands to hundreds of thousands of point data are used to represent point cloud content, then the accuracy and detail of the three-dimensional representation improve, but the encoding and decoding process becomes more time-consuming
Solution Approach 1:
The point cloud data is pre-processed and organized into a hierarchical structure of layers during encoding. This preliminary organization groups points by spatial location, depth, or other relevant criteria, allowing the decoder to efficiently access and process only the necessary data subsets. The pre-structured format enables faster decoding operations compared to processing raw, unorganized point cloud data.
Solution Approach 2:
The layered structure enables partial decoding where only a subset of layers is decoded based on available time, computational resources, or quality requirements. This allows the system to provide acceptable point cloud representation with reduced decoding time when necessary, while maintaining the capability for full high-accuracy reconstruction when resources are abundant.
3Adaptability or versatility
If a hierarchical structure with multiple octree depths is used to encode geometry data, then the scalability and adaptability of the encoding improve, but the device complexity and processing requirements increase
Solution Approach 1:
The encoding system dynamically selects which octree depths and layers to encode and transmit based on available bandwidth, storage capacity, and quality requirements. The hierarchical structure allows flexible adaptation where deeper octree levels (providing finer detail) can be included or excluded dynamically. This dynamic adaptability enables the same encoding framework to serve multiple quality and performance levels without requiring separate encoding systems.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A 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 information.