Geometry Point Cloud Inter Prediction with Reference Resampling
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Solution Overview
Problem
Existing point cloud compression techniques face inefficiencies in coding overhead due to inaccurate prediction of node parameters, particularly in the (r, ϕ, i) domain, which affects overall coding efficiency.
Innovation Solution
Implement inter prediction using motion compensated reference frames, selectively resampling points to generate a resampled reference frame for more accurate prediction of current points, reducing residual data size and improving coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If inter prediction is used to predict current points from reference frames, then coding efficiency is improved by reducing residual data size, but device complexity increases due to motion compensation and resampling operations
Solution Approach 1:
The reference frame is pre-resampled to generate a resampled reference frame before performing inter prediction. This preliminary action prepares the reference data in advance, allowing the prediction to use optimally matched points from the resampled reference frame, thereby reducing residual data size and improving coding efficiency without increasing the complexity of the prediction operation itself
Solution Approach 2:
The patent changes the parameter representation by converting point cloud coordinates into a (r, φ, i) domain (radius, azimuth, laser index) and performing motion compensation in this transformed domain. This parameter transformation enables more accurate motion modeling and prediction, reducing the bits needed for residual data while managing processing complexity through structured transformation operations
2Measurement precision
If motion compensated reference frames are used for prediction, then prediction accuracy is improved, but processing time increases due to additional resampling operations
Solution Approach 1:
The resampled reference frame is generated in advance before the prediction step, so that during actual prediction operations, the system can directly use the pre-prepared resampled reference frame without performing time-consuming resampling operations at prediction time. This separates the computationally intensive resampling step from the prediction step, improving prediction accuracy while minimizing processing time during encoding/decoding
Solution Approach 2:
The processing pipeline is segmented into distinct stages: reference frame resampling (preprocessing), motion compensation, and inter prediction. By segmenting these operations, the patent optimizes each stage independently - the resampling is performed once to create a reusable resampled reference frame, while prediction operations can proceed more quickly using the pre-prepared data, thus improving accuracy without proportionally increasing processing time
Data Source
AI summary
A device for encoding or decoding point cloud data may be configured to, responsive to determining to predict a current point in a current frame using an inter prediction mode: identify a reference frame; derive one or more motion compensated reference points from the reference frame; selectively resample points in the reference frame based on the one or more motion compensated reference points to generate a resampled reference frame; and inter predict the current point in the current frame based on the resampled reference frame.