Point Cloud Attribute Prediction Using Shifted Point Groups

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Solution Overview

Problem

Current point cloud coding technologies limit the performance of attribute prediction by restricting the obtaining of neighboring points based on a current point, reducing the efficiency of point cloud attribute prediction.

Innovation Solution

The method involves obtaining an alternative point set from multiple point cloud groups with identical coordinate code words after shifting by a grouping shift bit number, predicting a target point cloud point using prediction reference points, and decoding a code stream to determine the target attribute value.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If neighboring points are obtained based on a current point with limited spatial structure, then the complexity of point cloud processing is reduced, but the performance of point cloud attribute prediction deteriorates

Engineering Contradiction:

Improveprocessing complexityVSAvoidattribute prediction performance

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent transitions from limited local spatial structure to a more comprehensive prediction approach by incorporating alternative point sets from multiple point cloud groups. This dimensional expansion allows accessing prediction reference points beyond immediate neighbors, improving attribute prediction performance while maintaining manageable complexity through structured grouping and shifting operations.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Measurement precision

If alternative point sets from multiple point cloud groups are used for prediction, then the accuracy of attribute prediction is improved, but the computational complexity increases

Engineering Contradiction:

Improveattribute prediction accuracyVSAvoidcomputational complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the point cloud into multiple groups with identical coordinate code words after shifting by grouping shift bit numbers. This segmentation strategy organizes the computational workload into manageable segments, allowing efficient processing of alternative point sets while improving prediction accuracy through broader reference point selection.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent performs preliminary grouping and shifting operations to organize point cloud data before prediction. By pre-structuring the data into groups with identical coordinate code words and preparing alternative point sets in advance, the system reduces the computational burden during the actual prediction phase while maintaining high prediction accuracy.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250378587A1Point cloud prediction processing methods and apparatuses, computer, and storage medium

Publication Date: 2025.12.11 TENCENT TECHNOLOGY (SHENZHEN) CO LTD

AI summary

In a method for point cloud decoding, k coordinate code words of k point cloud points of a to-be-coded point cloud are obtained. The k point cloud points are grouped into M point cloud groups based on one or more grouping shift bit numbers. The coordinate code words of the point cloud points in each of the M point cloud groups are identical after shifting by a corresponding one of the one or more grouping shift bit numbers associated with the respective point cloud group. A quantity of the point cloud points in each of the M point cloud groups is equal to or less than a grouping unit threshold. Attributes of the k point cloud points are decoded based on the M point cloud groups.