Adaptive Depth Prediction Lists for Point Cloud Coding Gaps

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Solution Overview

Problem

Existing methods for coding and decoding point cloud depth information fail to consider scenario discontinuity, leading to low prediction accuracy and increased residuals, outliers, and hop values, affecting coding efficiency.

Innovation Solution

Create an adaptive prediction list for point cloud depth information using multiple candidate lists, including values from same and different lasers, historical points, and priori information, to select optimal predicted values and reduce residuals.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If only depth information of neighboring points is used for prediction, then the prediction process is simple, but prediction accuracy is low and coding efficiency deteriorates

Engineering Contradiction:

Improveprediction process complexityVSAvoidprediction accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The prediction process is segmented into multiple stages: first creating multiple candidate lists from different sources (same laser, different lasers, historical points), then selecting optimal candidates from these lists. This segmentation allows comprehensive information utilization while maintaining organized processing flow

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent extends the prediction from simple neighboring points to multiple dimensions including temporal (historical points), spatial (different lasers), and contextual (same laser) dimensions. This multi-dimensional approach significantly enriches the prediction information source

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Device complexity

If scenario discontinuity is not considered, then the prediction method is simple, but outliers and hop values increase affecting coding efficiency

Engineering Contradiction:

Improveprediction method complexityVSAvoidoutliers and hop values

Core Design Contradiction:

Device complexityVSLoss of information

Solution Approach 1:

The patent dynamically adapts to scenario changes by maintaining multiple candidate lists that can flexibly respond to discontinuities. When scenario discontinuity is detected, the system can switch between different prediction sources (same laser, different lasers, historical points) to maintain prediction accuracy

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent changes the parameter of prediction information sources by introducing multiple candidate lists with different characteristics. This allows the system to adjust prediction behavior based on scenario conditions, reducing outliers and hop values caused by scenario discontinuity

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20260067500A1Method and apparatus for predictively coding and decoding depth information of point cloud

Publication Date: 2026.03.05 HONOR DEVICE CO LTD

AI summary

A method for predictively coding depth information of a point cloud includes: obtaining original point cloud data, and creating an adaptive prediction list of the depth information of the point cloud. The method also includes predictively coding the depth information of the point cloud based on the adaptive prediction list, to obtain codestream information.