Hierarchical 3D Point Encoding with Depth-Based Prediction Modes

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Solution Overview

Problem

Existing three-dimensional data encoding methods lack efficiency in compressing and transmitting large amounts of point cloud data, necessitating improved coding techniques for effective representation and transmission.

Innovation Solution

A three-dimensional data encoding method that sets a prediction mode based on the depth of a three-dimensional point in a hierarchical structure, calculating a predicted value and a prediction residual, and generating a bitstream including this information to enhance coding efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If point cloud data is compressed using existing encoding methods, then data transmission and storage become feasible, but coding efficiency remains insufficient for massive point cloud datasets

Engineering Contradiction:

Improvecoding efficiencyVSAvoidamount of three-dimensional data

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

The point cloud data is divided into multiple regions based on depth information, with different prediction modes applied to different regions. This segmentation allows the encoder to handle different parts of the point cloud with appropriate strategies, improving overall coding efficiency for massive datasets

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Different prediction modes are selectively applied to different depth regions of the point cloud data. Specifically, first prediction modes are used for points within a certain depth range, while second prediction modes are used for points beyond that range, optimizing compression for each local region's characteristics

Inventive Principle:

Principle #3Local quality

2Measurement precision

If more prediction modes are used to improve coding precision, then accuracy of geometry information increases, but computational complexity and processing time increase

Engineering Contradiction:

Improveaccuracy of geometry informationVSAvoidcoding process complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

Different prediction modes are applied to different depth regions rather than using a single complex mode for all points. This approach achieves high accuracy for geometry information by tailoring the prediction strategy to each region's characteristics while keeping the overall system manageable through clear regional differentiation

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The prediction mode selection is based on depth parameter thresholds. By changing the prediction mode parameter according to the depth value of points, the system achieves adaptive precision optimization without requiring complex decision logic for each individual point

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12581117B2Three-dimensional data encoding method, three-dimensional data decoding method, three-dimensional data encoding device, and three-dimensional data decoding device

Publication Date: 2026.03.17 PANASONIC INTELLECTUAL PROPERTY CORP OF AMERICA

AI summary

A three-dimensional data encoding method for encoding three-dimensional points having a hierarchical structure, the three-dimensional data encoding method including: setting one prediction mode out of two or more prediction modes each for calculating a predicted value of an item of first geometry information of a first three-dimensional point using one or more items of second geometry information of one or more second three-dimensional points surrounding the first three-dimensional point; calculating a predicted value of the one prediction mode set; calculating a prediction residual that is a difference between the item of first geometry information and the predicted value calculated; and generating a first bitstream including the one prediction mode set and the prediction residual. In the setting, the one prediction mode is set based on a depth of the first three-dimensional point in the hierarchical structure.