Point Cloud Data Encoding Using Colorized Octree Structures
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Solution Overview
Problem
Existing methods for processing point cloud data face challenges with latency and encoding/decoding complexity, particularly when dealing with large datasets required for virtual reality, augmented reality, and self-driving services.
Innovation Solution
A method involving the encoding and decoding of point cloud data using a colorized octree structure, which efficiently represents geometry and attribute information, allowing for scalable processing and transmission by matching attributes to nodes in the octree structure and removing duplicates, enabling independent tile-by-tile or slice-by-slice processing to prevent errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If point cloud data is encoded and transmitted using traditional methods, then the data can be processed, but the processing latency is high and encoding/decoding complexity increases
Solution Approach 1:
The patent divides the point cloud data processing into independent tiles and slices, allowing parallel processing of different segments. The octree structure is divided into multiple levels that can be processed independently, reducing overall processing latency while maintaining manageable complexity for each segment.
Solution Approach 2:
The patent enables progressive decoding where attribute information can be decoded at different levels of detail. Users can receive basic geometry information first and then progressively add attribute information, reducing initial processing latency while allowing for more complex processing if needed.
2Manufacturing precision
If tens of thousands to hundreds of thousands of point data are used to represent point cloud content, then the quality of VR/AR/MR and self-driving services is improved, but the processing complexity and data volume increase significantly
Solution Approach 1:
The patent transforms the processing approach by organizing point cloud data into a hierarchical octree structure with multiple levels. This adds a dimensional organization to the data, allowing efficient navigation and processing of large datasets without increasing per-point complexity, as operations can be performed on aggregated node levels rather than individual points.
Solution Approach 2:
The patent implements nested processing where the octree structure contains multiple levels of hierarchy, with each node potentially containing child nodes. This nested organization allows coarse-level processing for overall structure and fine-level processing only where needed, reducing the effective complexity of processing hundreds of thousands of points by working with aggregated representations at higher levels.
3Loss of information
If the entire octree structure is fully encoded before transmission, then complete attribute information is provided, but the transmission time and processing latency increase
Solution Approach 1:
The patent encodes and transmits geometry information and basic octree structure first, allowing receivers to begin processing and rendering before all attribute information is received. Attribute information for different octree levels can be transmitted progressively, with higher-level attributes provided first for faster initial rendering, while maintaining the option to receive complete information later for enhanced quality.
Data Source
AI summary
A device for processing point cloud data according to embodiments may comprise the steps of: encoding point cloud data including geometry information and attribute information; and transmitting a bitstream including the encoded point cloud data.