Point Cloud Geometry Encoding With Spherical Inter-Frame Prediction
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing and transmitting large amounts of point cloud data, particularly in terms of latency, encoding/decoding complexity, and compression efficiency, especially for applications like virtual reality, augmented reality, and self-driving services.
Innovation Solution
A method and apparatus for point cloud data transmission and reception that involves encoding geometry data into a spherical coordinate system, performing inter-frame prediction, and compressing data based on similarity between frames, using a point cloud data transmission device with a geometry encoder and attribute encoder to transform and compress data, and a reception method that decodes and renders the data in a Cartesian coordinate system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If point cloud data is transmitted using traditional Cartesian coordinate systems without inter-frame prediction, then the encoding process is simple, but compression efficiency is poor and data volume is large
Solution Approach 1:
The patent transforms the coordinate system from Cartesian (x, y, z) to spherical (range, azimuth, elevation) to better represent point cloud data geometry. This parameter change enables more effective prediction of adjacent frame points, significantly improving compression efficiency by reducing redundant information while maintaining manageable encoding complexity through systematic transformation procedures.
Solution Approach 2:
The patent performs coordinate transformation from Cartesian to spherical coordinate system before compression encoding. This preliminary action prepares the data in a format that facilitates inter-frame prediction, allowing the system to exploit temporal redundancy more effectively and achieve better compression ratios without substantially increasing overall system complexity.
2Loss of information
If inter-frame prediction is performed on raw Cartesian coordinate data, then compression efficiency improves, but processing time increases causing latency
Solution Approach 1:
By transforming to spherical coordinate system, the patent reorganizes data parameters to match the natural geometry of point cloud surfaces. This enables more accurate prediction of adjacent frame points with fewer computational operations, improving compression ratio while actually reducing processing time compared to operating on raw Cartesian coordinates.
3Productivity
If coordinate transformation and inter-frame prediction are applied to all point cloud data, then compression efficiency is maximized, but encoding and decoding complexity increases
Solution Approach 1:
The systematic transformation to spherical coordinate system provides a structured approach that, while adding a transformation step, simplifies subsequent prediction operations. The regular mathematical relationships in spherical coordinates enable efficient algorithms that balance compression efficiency gains against the added encoding/decoding complexity, achieving optimal trade-off through standardized processing pipelines.
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, wherein the step of encoding the geometry data may comprise the steps of: converting coordinates of the geometry data from a first coordinate system to a second coordinate system; and compressing the geometry data by performing prediction between neighboring frames on the second coordinate system.