3D Point Cloud Encoding With Adaptive Neighbor Prediction

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Solution Overview

Problem

Existing three-dimensional data encoding methods face inefficiencies in compressing and transmitting large amounts of point cloud data, necessitating improved encoding efficiency for widespread use in applications like autonomous vehicles and robotics.

Innovation Solution

A three-dimensional data encoding method that selects one prediction mode from multiple modes based on attribute information of neighboring points to calculate a predicted value, generating a bit stream with the prediction residual, enhancing encoding efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If multiple prediction modes are provided for calculating predicted values of attribute information items, then encoding efficiency is improved, but device complexity increases

Engineering Contradiction:

Improveencoding efficiencyVSAvoidcomplexity of prediction mode selection

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements dynamic prediction mode selection where the encoder adapts the prediction mode based on the specific attribute information item being encoded and the characteristics of neighboring points. This allows the system to optimize encoding efficiency for each attribute type (e.g., color, intensity) while managing complexity through intelligent selection rather than processing all modes uniformly.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent changes the parameter of prediction mode selection based on attribute information characteristics. Different prediction modes are selected according to the type of attribute being encoded (color, intensity, etc.) and the spatial distribution of neighboring points, allowing the system to adapt to varying data characteristics and optimize compression efficiency.

Inventive Principle:

Principle #35Parameter changes

2Productivity

If prediction modes are selected based on attribute information items of neighboring points, then encoding efficiency is improved, but processing time increases

Engineering Contradiction:

Improveencoding efficiencyVSAvoidprocessing time for mode selection

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent performs preliminary analysis of neighboring points' attribute information before finalizing the prediction mode selection. By pre-examining the characteristics of adjacent points and preparing prediction candidates in advance, the system reduces the computational burden during the actual encoding process, thereby minimizing processing time while maintaining high encoding efficiency.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent applies partial action by selecting and evaluating only the most relevant prediction modes based on neighboring point characteristics, rather than exhaustively processing all possible modes. This selective approach maintains encoding efficiency while significantly reducing processing time by avoiding unnecessary computations for unlikely prediction modes.

Inventive Principle:

Principle #16Partial or excessive action

3Quantity of substance

If point cloud data is compressed for accumulation and transmission, then data quantity is reduced, but measurement precision may deteriorate

Engineering Contradiction:

Improveamount of three-dimensional dataVSAvoidprecision of attribute information

Core Design Contradiction:

Quantity of substanceVSMeasurement precision

Solution Approach 1:

The patent incorporates feedback mechanisms where the encoder continuously evaluates the compression results and adjusts prediction modes based on the actual data characteristics. This feedback loop ensures that compression achieves optimal balance between reducing data quantity and maintaining measurement precision by adapting to the specific properties of the point cloud data being processed.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent dynamically changes compression parameters including prediction mode selection and quantization settings based on the attribute information characteristics of the point cloud data. By adjusting these parameters adaptively, the system achieves effective compression of data quantity while preserving measurement precision for critical attributes such as color and spatial coordinates.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12395621B2Three-dimensional data encoding method, three-dimensional data decoding method, three-dimensional data encoding device, and three-dimensional data decoding device

Publication Date: 2025.08.19 PANASONIC INTELLECTUAL PROPERTY CORP OF AMERICA

AI summary

A three-dimensional data encoding method encodes a plurality of three-dimensional points, and includes: selecting one of two or more prediction modes for calculating a predicted value of an attribute information item of the first three-dimensional point, in accordance with attribute information items of one or more second three-dimensional points in the vicinity of a first three-dimensional point; calculating the predicted value by the selected prediction mode; calculating, as a prediction residual, a difference between a value of the attribute information item of the first three-dimensional point and the calculated predicted value; and generating a bit stream that includes the one prediction mode and the prediction residual.