Point Cloud Attribute Decoding With Kalman-Filtered Color Reconstruction

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Solution Overview

Problem

Existing point cloud compression methods face challenges in improving the prediction effect of color information during the encoding and decoding process, leading to suboptimal reconstruction accuracy.

Innovation Solution

Implement a Kalman filtering algorithm to filter initial reconstructed values of attribute information, such as color information, to obtain final reconstructed values, enhancing the decoding accuracy of point clouds.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional prediction methods are used for color information, then the encoding process is simple, but the reconstruction accuracy is insufficient

Engineering Contradiction:

Improvereconstruction accuracyVSAvoidfiltering algorithm complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent applies Kalman filtering which incorporates feedback mechanisms to continuously refine the predicted color values based on the difference between predicted and actual observed values, thereby improving reconstruction accuracy through iterative refinement

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent introduces dynamic parameters including prediction error covariance matrices and Kalman gain factors that adapt during the filtering process, allowing the system to optimize prediction accuracy by adjusting parameters based on observed data characteristics

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If more prediction information is processed, then the reconstruction accuracy improves, but the computational time increases

Engineering Contradiction:

Improveprediction accuracyVSAvoidcomputational time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary computations by pre-calculating the Kalman gain factors and prediction error covariance matrices during the encoding phase, so that during decoding only simpler operations are needed, reducing real-time computational burden while maintaining high prediction accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces complex iterative optimization mechanisms with the Kalman filtering algorithm that provides a closed-form solution, substituting computationally intensive iterative methods with a more efficient mathematical approach that achieves the same prediction accuracy faster

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12511791B2Point cloud decoding and encoding method, and decoder, encoder and encoding and decoding system

Publication Date: 2025.12.30 GUANGDONG OPPO MOBILE TELECOMMUNICATIONS CORP LTD

AI summary

A point cloud decoding method comprises: parsing a bitstream of a point cloud to obtain reconstructed information of position information of a target point in the point cloud; obtaining predicted values of attribute information of the target point according to the reconstruction information of the position information of the target point; parsing the bitstream to obtain residual values of the attribute information of the target point; obtaining initial reconstructed values of the attribute information of the target point according to the predicted values and the residual values of the attribute information of the target point; filtering the initial reconstructed values of the attribute information of the target point by using a Kalman filtering algorithm, to obtain final reconstructed values of the attribute information of the target point; and obtaining a decoded point cloud according to the final reconstructed values of the attribute information of the target point.