Point Cloud Frame Compression Using Inter-Frame Attribute Prediction
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Solution Overview
Problem
Uncompressed point cloud data formats require large data storage and hinder live rendering or streaming of dynamic 3D scenes, necessitating an efficient compression format.
Innovation Solution
Implementing inter-frame predictive encoding for point cloud data attributes, using preceding frames to encode differences, combined with intra-frame predictive encoding and octree structures for improved compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If uncompressed point cloud data formats are used, then complete definition of each point is achieved, but data storage requirement increases significantly
Solution Approach 1:
The patent changes the representation parameters from absolute values to relative differences. Instead of storing complete point definitions with absolute coordinates and attributes, the system stores only the differences between consecutive frames. This parameter transformation dramatically reduces data storage requirements while preserving the ability to reconstruct complete point cloud data through incremental updates.
2Measurement precision
If uncompressed point cloud data is used, then full point information is available, but live rendering or streaming becomes impossible
Solution Approach 1:
The system transforms the data representation from static complete definitions to dynamic incremental differences. This enables live rendering and streaming by allowing the receiver to reconstruct point cloud data frame-by-frame using only the transmitted difference data, making real-time processing feasible without requiring storage of complete uncompressed data sets.
3Quantity of substance
If inter-frame predictive encoding is used for point cloud attributes, then compression efficiency is improved, but encoding complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-identifying corresponding points between frames and pre-calculating attribute differences before encoding. The system establishes point correspondences and computes attribute variations in advance, which streamlines the encoding process and reduces runtime complexity despite the increased algorithmic sophistication required for inter-frame prediction.
Data Source
AI summary
A method for encoding a frame of a sequence of frames of point cloud data for rendering a dynamic 3D scene, wherein each frame comprises a plurality of points, and each point comprises a 3D position and one or more attributes, the method comprising: obtaining a current point to be encoded in a current frame; identifying a preceding point encoded in a preceding frame which corresponds to the current point to be encoded in the current frame; encoding at least one attribute of the current point by reference to the preceding point using inter-frame predictive encoding to generate encoded data for the current point; and generating the current frame comprising the encoded data for the current point.